Hematology Flow System Interface

Abstract

A sample analysis system may include a flow cell, a fluidics system, an image capture device, and a processor. In such a system, the fluidics system may be adapted to flow a portion of the sample through the flow cell, and the image capture device may be configured to capture multiple images of blood cells in the flow cell. The processor may be programmed to perform multiple actions, including generating result data, displaying an interface operable to a user for selecting parameters, and displaying information corresponding to instructions for reviewing the blood cells or the sample.

Description

[Technical Field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This claim claims priority from and is a nonprovisional application of Provisional Patent Application No. 63 / 430,237, entitled "Hematology Flow System Interface," filed in the United States Patent and Trademark Office on December 5, 2022, which is incorporated by reference in its entirety. [Background technology]

[0002] Blood cell analysis is one of the most commonly performed medical tests to provide an overview of a patient's health status. A blood sample may be drawn from a patient's body and stored in a test tube containing an anticoagulant to prevent clotting. A whole blood sample typically contains three major classes of blood cells, including red blood cells (erythrocytes), white blood cells (leukocytes), and platelets (thrombocytes). Each class can be further divided into subclasses of elements. For example, the five major types or subclasses of white blood cells (WBCs) have different shapes and functions. White blood cells may include neutrophils, lymphocytes, monocytes, eosinophils, and basophils. Subclasses of red blood cell types also exist. The appearance of particles within a sample may vary depending on pathological conditions, cell maturity, and other factors. Red blood cell subclasses may include reticulocytes and nucleated red blood cells.

[0003] Traditional blood cell analysis techniques have used principles such as impedance or Coulter principle and fluorescence or light scattering to count and measure cells. These techniques utilize indirect measurements and therefore may be limited in the quantity and quality of information they can provide. Furthermore, slide review is a common secondary step; test results will require further analysis (e.g., to confirm the results or to evaluate some abnormalities), which is typically performed through automated or manual slide imaging steps.

[0004] There is a need for improvements to traditional blood cell analysis techniques that optimize workflow and leverage new techniques to improve cell analysis to improve patient outcomes. Summary of the Invention

[0005] Described herein are devices, systems, and methods for classifying objects, such as cells, using an analyzer, such as a biological analyzer / biological analysis system, that captures cellular images. In some embodiments, images of blood cells from a blood sample and additional values ​​(e.g., impedance-derived values, volume-conductivity-scatter-derived values, fluorescence-derived values, and / or spectrophotometry-derived values) may both be used in such classification or other types of analysis. In some embodiments, the images, image-derived values, and values ​​derived from non-imaging techniques are presented on a user interface (e.g., a screen).

[0006] In some embodiments, cellular information obtained from images and cellular information obtained through non-imaging techniques (e.g., impedance, fluorescence, or spectrophotometry) may overlap, for example, when imaging is used to obtain a first parameter (e.g., red blood cell count or platelet count) of a first particle and non-imaging is similarly used to obtain a parameter (e.g., red blood cell count or platelet count). In some embodiments, both values ​​are presented on the user interface.

[0007] In some embodiments, a sample analysis system may be provided that includes a fluidics system and one or more processors. In such a system, the fluidics system may be adapted to flow a first portion of a blood sample through a first module, the first module being a flow imaging module including a flow cell and an image capture device configured to capture multiple images of cells in the first portion of the blood sample. The fluidics system may be adapted to flow a second portion of the blood sample through a second module, the second module being configured to test one or more numerical parameters of the cells in the second portion of the blood sample. The one or more processors may be programmed to perform a set of actions. These actions may include determining one or more numerical parameters of the cells in the second portion of the blood sample and presenting a computing interface including multiple images of the cells in the first portion of the blood sample and the one or more numerical parameters of the cells in the second portion of the blood sample. Corresponding methods and computer-readable media may be implemented based on the present disclosure. Therefore, the system as described should be understood as merely illustrative and should not be treated as imposing limitations on the protection provided by this document or any related document.

[0008] In some embodiments, the imaging system utilizes image analysis algorithms to analyze the cell images and report specific information about the cells, such as cell type, cell count, or other quantitative information about the cells. The algorithms can, for example, utilize trained machine learning algorithms or pixel analysis to analyze the images.

[0009] In some embodiments, the biological analysis system provides review instructions (e.g., flags) associated with the analyzed biological sample. For example, the review instructions may be associated with any of the reported counts of specific cell types, abnormal results, and abnormal cell types.

[0010] In some embodiments, the biological analysis method includes image review on a user interface, where a user can confirm sample results through the user interface image review. In some embodiments, the biological analysis method includes analyzing a biological sample, presenting images of cells of the biological sample on the user interface, and confirming sample results through the user interface image review. In some embodiments, the user interface image review includes review instructions (e.g., flags) associated with the biological sample being analyzed.

[0011] In some embodiments, a multi-channel analyzer or multi-channel analysis system comprises an imaging channel or module and one or more non-imaging channels, for example, utilizing any of impedance, volume-conductivity-scatter, fluorescence, or spectrophotometry.

[0012] In some embodiments, the methods of the embodiments described above and herein are contemplated.

[0013] While the specification concludes with claims which particularly point out and distinctly claim the invention, it is believed the invention will be better understood by reading the following description of specific examples in conjunction with the accompanying drawings in which like reference numerals identify the same elements and in which: [Brief explanation of the drawings]

[0014] [Figure 1-1]1 is a schematic diagram, partially in cross section and not to scale, illustrating the operation of an exemplary flow cell autofocus system and high optical resolution imaging device for sample image analysis using digital image processing. [Figure 1-2] 1A and 1B illustrate an optical bench arrangement according to various embodiments, and another optical bench arrangement according to various embodiments, respectively. [Figure 1-3] FIG. 1C is a block diagram of a blood analyzer according to various embodiments. [Figure 2] 1A-1C are schematic diagrams illustrating aspects of a cellular analysis system according to various embodiments. [Figure 3] 1 is a system block diagram illustrating aspects of a cellular analysis system according to various embodiments. [Figure 4] FIG. 1 illustrates aspects of an automated cellular analysis system for assessing an individual's white blood cell status, in accordance with an embodiment of the present invention. [Figure 5] FIG. 1 illustrates a process for deriving data from captured images and measured impedance, according to various embodiments. [Figure 6] FIG. 1 illustrates a process for reviewing data derived from captured images, according to various embodiments. [Figure 7] FIG. 1 illustrates an exemplary user interface, according to various embodiments. [Figure 8] FIG. 10 illustrates another process for deriving data from captured images and measured impedance, according to various embodiments. [Figure 9] FIG. 1 illustrates a modular system that may be utilized in some implementations of the disclosed technology. [Figure 10] FIG. 1 is a perspective view of an exemplary optical system of a fluorescence analyzer. [Figure 11] FIG. 1 illustrates a process that may be used to stain a sample. [Figure 12]1 is a system block diagram illustrating aspects of a cellular analysis system, in accordance with an embodiment of the present invention. [Figure 13] FIG. 1 illustrates a spectrophotometry system utilized in some implementations of the disclosed technology. [Figure 14] FIG. 14 illustrates a method of using the spectrophotometry system of FIG. 13. [Figure 15] FIG. 1 illustrates a dual channel test fixture having an imaging system and a non-imaging system. [Figure 16] 16 is a schematic diagram of the non-imaging system of FIG. 15. [Figure 17] FIG. 16 is a diagram illustrating the imaging system of FIG. [Figure 18] FIG. 1 illustrates an architecture for a machine learning model that may be used when analyzing images. [Figure 19] FIG. 19 illustrates an example of stages that may be included in a machine learning model according to the architecture of FIG. 18. DETAILED DESCRIPTION OF THE INVENTION

[0015] The drawings are not intended to be limiting in any way, and it is contemplated that various embodiments of the invention may be embodied in a variety of other ways, including ways not necessarily shown in the drawings. The accompanying drawings, which are incorporated in and form a part of this specification, illustrate several aspects of the invention and, together with the description, serve to explain the principles of the invention, it being understood, however, that the invention is not limited to the precise arrangements shown.

[0016] The present disclosure relates to devices, systems, compositions, and methods for analyzing samples containing particles. One embodiment may include an automated particle imaging system including an analyzer, which may be, for example, a visual analyzer. In some embodiments, the visual analyzer may further include a processor to facilitate automated analysis of the image.

[0017] Further embodiments can include other particle analysis systems along with the visual analyzer. These other particle analysis systems can include, for example, automated impedance measurement systems, fluorescence measurement systems, spectrophotometric measurement systems, conductivity systems, light scattering systems, additional imaging systems, or other types of systems that may be used to collect data about a sample. In some embodiments, the analyzer may further include a processor to facilitate automated analysis of images and / or to present one or more interfaces capable of presenting data from multiple channels (e.g., an interface capable of presenting data derived from images captured by an imaging device and data derived from measurements performed by one or more of the impedance, conductivity, light scattering, fluorescence, or spectrophotometry systems). In some embodiments, the biological analyzer or biological analysis system includes multiple channels or modules—including an imaging channel / module and one or more non-imaging channels / modules (e.g., impedance, conductivity, scattering, fluorescence, spectrophotometry).

[0018] Imaging system According to some aspects of the present disclosure, a system including a visual / imaging analyzer or module may be provided for obtaining images of a sample containing particles suspended in a liquid. Such a system may be useful in characterizing particles in biological fluids, such as detecting and quantifying erythrocytes, reticulocytes, nucleated red blood cells, platelets, and white blood cells, including, for example, white blood cell differential counting, categorization and subcategorization, and analysis. Other similar uses, such as characterizing blood cells from other fluids, are also contemplated.

[0019] Although the discrimination and / or classification of blood cells within a blood sample is an exemplary application for which the subject matter is particularly well suited, other types of bodily fluid samples may be used. For example, embodiments of the disclosed technology may be used in the analysis of non-blood bodily fluid samples containing blood cells (e.g., white blood cells and / or red blood cells), such as serum, bone marrow, lavage fluid, effusion, exudate, cerebrospinal fluid, pleural fluid, peritoneal fluid, and amniotic fluid. It is also possible that the sample can be a solid tissue sample (e.g., a biopsy sample processed to create a cell suspension). The sample may also be a suspension obtained from processing a fecal sample or a urine sample. The sample may also be a laboratory or production line sample containing particles, such as a cell culture sample. The term sample may be used to refer to a sample obtained from a patient or laboratory, or any fraction, portion, or aliquot of a sample. The sample may be diluted, divided into portions, or stained in some process.

[0020] In some embodiments, samples are presented, imaged, and analyzed in an automated manner. In the case of blood samples, the sample may be significantly diluted with an appropriate diluent or saline solution, which reduces the extent to which the view of some cells is obscured by other cells in undiluted or less diluted samples. Cells may be treated with agents that enhance the contrast of some cellular aspects, for example, permeabilizing agents that permeabilize cell membranes and histological stains that adhere to and reveal features such as granules and nuclei. In some cases, it may be desirable to stain an aliquot of the sample to count and characterize particles, including reticulocytes, nucleated red blood cells, and platelets, and for white blood cell differential, characterization, and analysis. In other cases, samples containing red blood cells may be diluted prior to introduction into the flow cell and / or imaging or otherwise in the flow cell.

[0021] Referring now to FIG. 1 , a schematic example of a flow cell 22 is shown. In some embodiments, the flow cell 22 may transport a sample fluid through an observation zone 23 of a high-optical resolution imaging device 24 in a configuration for imaging microparticles within a sample flow stream 32 using digital image processing. The flow cell 22 may be coupled to a source 25 of sample fluid that may have undergone processing, such as contact with a particle contrast agent composition and heating. The flow cell 22 is also coupled to one or more sources of particle and / or intracellular organelle alignment liquid (PIOAL) 27, also known as a sheath fluid, such as a clear glycerol solution having a viscosity greater than that of the sample fluid. In some embodiments, the PIOAL includes an iminodiac, multiple salts, bronidoxine, glycerol, and polyvinylpyrrolidone (PVP). Further information regarding PIOAL / sheath fluids is provided in U.S. Patent No. 9,316,635, entitled "Sheath fluid systems and methods for particle analysis in blood samples," issued April 19, 2016, the disclosure of which is incorporated by reference in its entirety.

[0022] Sample fluid is injected into the flow cell 22 through a flat opening at the distal end 28 of the sample supply tube 29 and at a point where PIOAL flow is substantially established, resulting in a stable, symmetric laminar flow of PIOAL above and below (or on opposite sides of) the ribbon-shaped sample stream 32. The sample and PIOAL streams may be supplied by precision metering pumps, which move the PIOAL, along with the injected sample fluid, along a significantly narrowing flow path. The PIOAL surrounds and compresses the sample fluid within the flow path narrowing zone 21. Thus, the reduction in flow path thickness in zone 21 can contribute to the geometric focusing of the sample stream 32. The sample fluid ribbon 32 is surrounded and transported along with the PIOAL downstream of the narrowing zone 21, passing in front of or otherwise through an observation zone 23 of a high-optical resolution imaging device 24 where images are collected, for example, using a CCD 48. Flow imaging is thus performed, in which images are collected from the flowing sample stream and the cellular material contained therein. The processor 18 may receive as input pixel data from the CCD 48. The sample fluid ribbon flows to the outlet 33 along with the PIOAL.

[0023] 1, narrowing zone 21 can have proximal flow path portion 21a having a proximal thickness PT and distal flow path portion 21b having a distal thickness (DT), whereby distal thickness DT is less than proximal thickness (PT). Thus, sample fluid can be injected through distal end 28 of sample tube 29 at a location distal to proximal portion 21a and proximal to distal portion 21b. Thus, sample fluid can enter the PIOAL envelope when the PIOAL stream is compressed by zone 21. The sample fluid injection tube has a distal exit port through which sample fluid is injected into the flowing sheath liquid, the distal exit port being surrounded by a reduced flow path size portion of the flow cell.

[0024] A digital high-light resolution imaging device 24 having an objective lens 46 is oriented along an optical axis that intersects the ribbon-shaped sample stream 32. The relative distance between the objective lens 46 and the flow cell 22 is variable by operation of a motor drive 54 to resolve and collect a focused, digitized image on a photosensor array. Further information regarding the configuration and operation of an exemplary flow cell, such as that shown in FIG. 1, is provided in U.S. Pat. No. 9,322,752, entitled "Flowcell Systems and Methods for Particle Analysis in Blood Samples," issued April 26, 2016, the disclosure of which is incorporated by reference in its entirety, and / or U.S. Pat. No. 9,857,361, entitled "Flowcell, Sheath Fluid, and Autofocus Systems and Methods for Particle Analysis in Urine Samples," issued January 2, 2018, the disclosure of which is incorporated by reference in its entirety. The embodiment of FIG. 1 illustrates a flow imaging system in which cells are imaged under flow through the flow cell 22.

[0025] Some embodiments may implement techniques for automatically achieving a precise operating position of the high-optical resolution imaging device 24 for focusing the ribbon-shaped sample stream 32. The flow cell structure 22 may be configured so that the ribbon-shaped sample stream 32 has a fixed and reliable location within the flow cell, defining a flow path for the sample fluid in a thin ribbon between layers of the PIOAL that passes through the observation zone 23 within the flow cell 22. In certain flow cell embodiments, the cross-section of the flow path for the PIOAL narrows symmetrically at the point where the sample is inserted through a flat orifice, such as a tube 29 with a rectangular lumen at the orifice, or a cannula. The narrowed flow path (e.g., a geometric narrowing in cross-sectional area by a ratio of 20:1 or between 20:1 and 70:1), along with the difference in viscosity between the PIOAL and the sample fluid and, optionally, the difference in linear velocity of the PIOAL compared to the sample flow, cooperate to compress the sample cross-section by a ratio of approximately 20:1 to 70:1. In some embodiments, the cross-sectional thickness ratio may be 40:1.

[0026] In one embodiment, the symmetrical nature of flow cell 22 and the method of injection of the sample fluid and PIOAL provide a reproducible location within flow cell 22 for ribbon-shaped sample stream 32 between the two layers of PIOAL. As a result, process variations, such as the inherent linear velocity of the sample and PIOAL, do not tend to displace the ribbon-shaped sample stream from its location within the flow. Due to the structure of flow cell 22, the location of ribbon-shaped sample stream 32 is stable and reproducible.

[0027] However, the relative positions of the flow cell 22 and the high optical resolution imaging device 24 in the optical system may be subject to change and may benefit from occasional positional adjustments to maintain an optimal or desired distance between the high optical resolution imaging device 24 and the ribbon-shaped sample stream 32 and therefore provide good quality focused images of particles enclosed within the ribbon-shaped sample stream 32.

[0028] According to some embodiments, there may be an optimal or desired distance between the high-light resolution imaging device 24 and the ribbon-shaped sample stream 32 to obtain a focused image of the enclosed particle. First, the optical elements may be precisely positioned relative to the flow cell 22 by autofocus or other techniques to position the high-light resolution imaging device 24 at an optimal or desired distance from an autofocus target 44, which has a fixed position relative to the flow cell 22. The displacement distance between the autofocus target 44 and the ribbon-shaped sample stream 32 is precisely known, for example, as a result of an initial calibration step. After autofocusing on the autofocus target 44, the flow cell 22 and / or the high-light resolution imaging device 24 are then displaced over the known displacement distance between the autofocus target 44 and the ribbon-shaped sample stream 32. As a result, the objective lens of the high-light resolution imaging device 24 is precisely focused on the ribbon-shaped sample stream 32 containing the enclosed particle.

[0029] Some embodiments may include autofocusing on a focus or imaging target 44, which is a high-contrast image defining a known location along the optical axis of the high-light resolution imaging device or digital image capture device 24. The target 44 may have a known displacement distance relative to the location of the ribbon-shaped sample stream 32. A contrast measurement algorithm may be used specifically for the target feature. In one example, the position of the high-light resolution imaging device 24 may be varied along a line parallel to the optical axis of the high-light resolution imaging device or digital image capture device to find the depth or distance at which one or more maximum differential amplitudes are found between pixel luminance values ​​occurring along a line of pixels in the image known to cross the edge of the contrast image. In some cases, the autofocus pattern does not have variation along a line parallel to the optical axis, which is also the line along which the motorized control operates to adjust the position of the high-light resolution imaging device 24 to provide the recorded displacement distance.

[0030] Thus, it may not be necessary to autofocus or rely on image content aspects that are variable between different images, which may not be highly defined in terms of contrast or may be located anywhere in a range of positions, as a basis for determining a reference distance location. Having found an optimal or desired focus location for the autofocus target 44, the relative positions of the objective lens 46 of the high optical resolution imaging device and the flow cell 22 can be displaced by the recorded displacement distance to provide an optimal or desired focus position for the particles in the ribbon-shaped sample stream 32.

[0031] According to some embodiments, the high light resolution imaging device 24 is able to resolve an image of the ribbon-shaped sample stream 32 because it is backlit by a light source 42 through an illumination aperture (window) 43. In the embodiment shown in Figure 1, the outer periphery of the illumination aperture 43 forms an autofocus target 44. However, the goal is to collect a precisely focused image of the ribbon-shaped sample stream 32 through high light resolution imaging device optics 46 onto an array of photosensitive elements, such as integrated charge-coupled devices.

[0032] The high-light resolution imaging device 24 and its optics 46 are configured to resolve images of particles in the ribbon-shaped sample stream 32 focusing at a distance 50, which can be a result of the dimensions of the optics, the shape of the lenses, and the refractive index of their materials. In some cases, the optimal or desired distance between the high-light resolution imaging device 24 and the ribbon-shaped sample stream 32 does not change. In other cases, the distance between the flow cell 22 and the high-light resolution imaging device and its optics 46 can be changed. Moving the high-light resolution imaging device 24 and / or the flow cell 22 closer to or farther from each other (e.g., by adjusting the distance 50 between the imaging device 24 and the flow cell 22) moves the location of the focusing point to the end of the distance 50 relative to the flow cell.

[0033] In some embodiments, the focus target 44 can be located at a distance from the ribbon-shaped sample stream 32, in this case affixed directly to the flow cell 22 at the edge of the opening 43 for light from the illumination source 42. The focus target 44 is at a fixed displacement distance 52 from the ribbon-shaped sample stream 32. Often, the displacement distance 52 is constant because the location of the ribbon-shaped sample stream 32 within the flow cell remains constant.

[0034] An exemplary autofocus procedure involves adjusting the relative positions of the high-light resolution imaging device 24 and the flow cell 22 using motor 54 to achieve the proper focal length, thereby focusing the high-light resolution imaging device 24 on the autofocus target 44. By way of example, the relative position adjustment is performed by moving one or more of the imaging device 24, the flow cell 22, or the imaging device's objective lens to change the relative position between the imaging device 24 and the flow cell 22. In this example, the autofocus target 44 is behind the ribbon-shaped sample stream 32 within the flow cell. The high-light resolution imaging device 24 is then moved toward or away from the flow cell 22 until the autofocus procedure establishes that the image resolved on the optical sensor is a precisely focused image of the autofocus target 44. Motor 54 is then operated to displace the relative positions of high-light resolution imaging device 24 and flow cell 22 to focus ribbon-shaped sample stream 32 by moving high-light resolution imaging device 24 away from flow cell 22, specifically by the span of displacement distance 52. In this exemplary embodiment, imaging device 24 is shown moved by motor 54 to reach the focused position. In another embodiment, the objective lens of imaging device 24 is moved. In other embodiments, flow cell 22 is moved, or both flow cell 22 and imaging device 24 are moved, by similar means to obtain a focused image.

[0035] These directions of movement would be reversed if the focus target 44 were positioned on the front viewport window opposite the rear illumination window 43. In that case, the displacement distance would be the span between the ribbon sample stream 32 and the target 44 (not shown) in the front viewport.

[0036] A displacement distance 52, which is equal to the distance between the ribbon-shaped sample stream 32 along the optical axis of the high-light resolution imaging device 24 and the autofocus target 44, may be established in a factory calibration step or by a user. Typically, once established, the displacement distance 52 does not change. Thermal expansion variations and vibrations may cause the precise positions of the high-light resolution imaging device 24 and the flow cell 22 to shift relative to one another, thus requiring a restart of the autofocus process. However, autofocusing on the target 44 provides a position reference that is fixed relative to the flow cell 22 and, therefore, relative to the ribbon-shaped sample stream 32. Similarly, the displacement distance is constant. Thus, by autofocusing on the target 44 and displacing the high-light resolution imaging device 24 and the flow cell 22 by the span of the displacement distance, the result is that the high-light resolution imaging device focuses on the ribbon-shaped sample stream 32.

[0037] According to some embodiments, the focus target 44 is provided as a high-contrast circle printed or attached around the illumination aperture 43. Alternative focus target configurations are discussed elsewhere herein. When a square or rectangular image is collected with the target 44 in focus, a high-contrast boundary appears around the center of the illumination. Searching for the position at the inner edge of the aperture where the highest contrast is obtained in the image automatically focuses the high-light resolution imaging device 24 to the working location of the target 44. According to some embodiments, the term “working distance” can refer to the distance between the objective lens and its focal plane, and the term “working location” can refer to the focal plane of the imaging device. The highest contrast measure of the image is where the brightest white measurement pixel and the darkest black measurement pixel are adjacent to each other along a line passing through the inner edge. The highest contrast measure can be used to evaluate whether the focal plane of the imaging device 24 is in a desired position relative to the target 44.

[0038] Other autofocus techniques, such as edge detection, image segmentation, and integrating the amplitude difference between adjacent pixels and finding the maximum sum of the differences, can also be used. In one technique, the sum of the differences is calculated at three distances encompassing the operating position on either side of the target 44, and the resulting values ​​are fitted to a characteristic curve, with the optimum distance being at the peak value on the curve. Relatedly, an exemplary autofocus technique can include collecting images of the flow cell target at different positions and analyzing the images to find the best focus position, using an index that is greatest when the image of the target is sharpest. During a first step (e.g., a coarse step), the autofocus technique can operate to find a preliminary best position from a set of images collected at 2.5 μm intervals. From that position, the autofocus technique can then include collecting a second set of (fine) images at 0.5 μm intervals and calculating a final best focus position for the target.

[0039] In some cases, a focus target 44 (e.g., an autofocus pattern) can be present around the observation area where the sample appears. It is also possible that the focus target 44 could be defined by contrasting shapes present in the field of view. Typically, the autofocus target 44 is mounted on the flow cell 22 or rigidly attached in a fixed position relative to the flow cell. Under the power of a positioning motor 54 controlled by a detector (e.g., processor 18) in response to maximizing the contrast of the image of the autofocus target, the device autofocuses on the target 44, as opposed to the ribbon-shaped sample stream. Then, by displacing the flow cell 22 and / or the high-light resolution imaging device 24 relative to each other by a displacement distance known to be the distance between the autofocus target 44 and the ribbon-shaped sample stream 32, the operating position or focal plane of the high-light resolution imaging device is displaced from the autofocus target to the ribbon-shaped sample stream. As a result, the ribbon-shaped sample stream 32 appears in focus in the collected digital image.

[0040] In some embodiments, a further focusing step is used after the target autofocus step. For example, focusing on the target is the first step to establish the overall position of the camera relative to the flow cell / flow cell target. A further step can utilize real-time focusing on the imaged sample (e.g., blood cells). One example includes pixel binning analysis between the V / brightness-values ​​of red or white blood cells to establish the ideal focus location, and comparison of V values ​​between various bins. Alternatively, after the target is used to set the camera location relative to the flow cell / flow cell target, a focus evaluation step can occur to measure the focus quality of post-acquisition images—such as using the V / brightness values ​​or red or white blood cells described herein—to monitor the camera focus position over time. Further information regarding autofocus approaches that may be implemented in some embodiments is provided in U.S. Pat. No. 9,857,361, U.S. Pat. No. 10,705,008, U.S. Pat. No. 10,705,011, International Patent Application No. PCT / US2022 / 052702, and International Patent Application No. PCT / US2023 / 011759, the contents of each of which are incorporated by reference in their entirety.

[0041] In order to distinguish particle types by data processing techniques, such as red blood cell and white blood cell categories and / or subcategories, it is advantageous to record micro-pixel images with sufficient resolution and clarity to reveal aspects that distinguish one category or subcategory from another.

[0042] In certain embodiments, the apparatus can be based on an optical bench arrangement as shown in FIG. 1A and enlarged in FIG. 1B , which has an illumination source 42 oriented over a flow cell 22 mounted in a gimbaled or flow cell holder 55 to backlight the contents of the flow cell 22 in images acquired by a high-light resolution imaging device 24. The holder 55 is mounted on a motor drive for precise movement toward and away from the high-light resolution imaging device 24. The holder 55 also enables precise alignment of the flow cell 22 with respect to the optical viewing axis of the high-light resolution imaging device or digital image capture device 24 so that the ribbon-shaped sample stream flows in a plane perpendicular to the viewing axis within the zone where the ribbon-shaped sample stream is imaged, i.e., the zone between the illumination aperture 43 and the observation port 57 as shown in FIG. 1 . A focusing objective 44 can assist in adjusting the holder 55, for example, to establish a plane of the ribbon-shaped sample stream perpendicular to the optical axis of the high-light resolution imaging device or digital image capture device.

[0043] Thus, the holder 55 may provide very precise linear and angular adjustment of the position and orientation of the flow cell 22 relative to, for example, the image capture device 24 or the image capture device objective. As shown here, the holder 55 may include two pivot points 55a and 55b to facilitate angular adjustment of the holder and flow cell 22 relative to the image capture device 24. The angular adjustment pivot points 55a and 55b may be located in the same plane and centered on the flow cell 22 channel (e.g., at the image capture site). This allows for angular adjustment without causing any linear translation of the flow cell 22 position. The holder 55 may be rotated about the axis of pivot point 55a, about the axis of pivot point 55b, or about both axes. Such rotation may be controlled by the processor 18 and a flow cell movement control mechanism (e.g., motor 54).

[0044] 1B , it can be seen that one or both of image capture device 24 and / or holder 55 (with flow cell 22) can be rotated or translated in three dimensions along various axes (e.g., X, Y, Z). Thus, in some embodiments, a technique for adjusting the focus of an image capture device may include implementing axial rotation of image capture device 24 about the imaging axis, for example, by rotating the device about axis X. In further embodiments, focus adjustment can also be achieved by axial rotation of flow cell 22 and / or holder 55 about an axis extending along the imaging axis, for example, about axis X, and within the field of view of imaging device 24.

[0045] In some cases, the focus adjustment may include tip rotation of the image capture device (e.g., rotation about axis Y). In other cases, the focus adjustment may include tip rotation of the flow cell 22 (e.g., rotation about axis Y or about pivot point 55a). As shown here, pivot point 55a corresponds to the Y axis extending along and within the flow path of the flow cell. In some cases, the focus adjustment can include tilt rotation of the image capture device (e.g., rotation about axis Z). In other cases, the focus adjustment may include tilt rotation of the flow cell 22 (e.g., rotation about axis Z or about pivot point 55b). As shown in FIG. 1B, pivot point 55b corresponds to the Z axis transverse to the flow path and imaging axis. In some cases, the image capture device 24 can focus the sample flow stream by implementing rotation of the flow cell 22 (e.g., about axis X) to center the rotation within the field of view of the image capture device. The three-dimensional rotational adjustments described herein may be implemented to address positional drift in one or more components of the analyzer system. In some embodiments, the three-dimensional rotational adjustments may be implemented to address temperature variations in one or more components of the analyzer system. In further embodiments, adjustment of the analyzer system may include translating the imaging device 24 along axis X. Additionally, in some embodiments, adjustment of the analyzer system may include translating the holder 55 or the flow cell 22 along axis X. More information regarding such holders that may be utilized in some embodiments is provided in U.S. Patent Application No. 18 / 224,953, the disclosure of which is incorporated by reference in its entirety.

[0046] Thus, according to one or more embodiments disclosed herein, a visual analyzer for obtaining an image of a sample containing particles suspended in a liquid includes a flow cell 22 coupled to a sample source 25 and a PIOAL material source 27, as shown in FIG. 1 . The flow cell 22 may define an internal flow path that narrows symmetrically in the direction of flow. The flow cell 22 is configured to direct a flow 32 of the sample surrounded by the PIOAL through an observation zone within the flow cell, i.e., after an observation port 57. Still referring back to FIG. 1 , a digital high-light resolution imaging device 24 having an objective lens 46 may be oriented along an optical axis that intersects the ribbon-shaped sample stream 32. The relative distance between the objective lens 46 and the flow cell 22 may be varied by operation of a motor drive 54 to resolve and collect a focused, digitized image on a photosensor array.

[0047] An autofocus target 44, having a fixed position relative to the flow cell 22, is located at a displacement distance 52 from the plane of the ribbon-shaped sample stream 32. In the embodiment shown, the autofocus target 44 is attached directly to the flow cell 22 at a location that is visible in images collected by the high light resolution imaging device 24. In another embodiment, if the autofocus target is not integrally attached directly to the body of the flow cell, it may be carried on a part that is rigidly fixed in position relative to the flow cell 22 and the ribbon-shaped sample stream 32 therein.

[0048] Light source 42, which may be a steady light source or a strobe that flashes in sync with the operation of a high-light resolution imaging device light sensor, is configured to illuminate ribbon-shaped sample stream 32 and also contribute to the contrast of target 44. In the embodiment shown, the illumination is from a backlight. In some examples, light source 42 may include a single light (e.g., an LED) or multiple lights (e.g., three LEDs—one green, one red, and one blue that combine to create a single white light). Further information regarding how illumination may be provided in some implementations is provided in U.S. Patent Application Serial No. 18 / 224,937, the disclosure of which is incorporated by reference in its entirety.

[0049] 1C, a block diagram of a further embodiment of a blood analyzer 100c is shown. In some embodiments, and as shown, analyzer 100c may include at least one digital processor 18 coupled to operate motor drive 54 and to analyze digitized images from the photosensor array collected at different focus positions relative to target autofocus pattern 44. Processor 18 is configured to determine the focus position of autofocus pattern 44 (e.g., to autofocus on target autofocus pattern 44 and thus establish an optimal distance between high light resolution imaging device 24 and autofocus pattern 44). In some embodiments, this may be achieved by an image processing step, such as applying an algorithm to evaluate the level of contrast in the image at a first distance, which may be applied to the entire image or at least the edge of autofocus pattern 44. The processor moves motor 54 to different positions, evaluates the contrast at that position or edge, and after two or more iterations, determines the optimal distance that maximizes the accuracy of focus on autofocus pattern 44 (or that, when moved to that position, will optimize the accuracy of focus). The processor may rely on a fixed spacing between the autofocus target 44 and the ribbon-shaped sample stream 32, and the processor 18 may then control the motor 54 to move the high light resolution imaging device 24 to the correct distance to focus on the ribbon-shaped sample stream 32. More specifically, the processor 18 may operate the motor 54 to displace the distance 50 between the high light resolution imaging device 24 and the ribbon-shaped sample stream 32 by a displacement distance 52 (e.g., shown in FIG. 1 ) at which the ribbon-shaped sample stream is displaced from the target autofocus pattern 44. In this way, the high light resolution imaging device is focused on the ribbon-shaped sample stream.

[0050] The flow cell internal contour and the PIOAL and sample flow rates can be adjusted to form the sample into a ribbon-shaped stream 32. The stream can be approximately as thin as or even thinner than the particles encased within the ribbon-shaped sample stream. White blood cells, for example, may have a diameter of approximately 10 μm. By providing a ribbon-shaped sample stream 32 with a thickness less than 10 μm, cells may be oriented as the ribbon-shaped sample stream is stretched by the sheath fluid or PIOAL. Surprisingly, stretching the ribbon-shaped sample stream along the narrowed flow path within a PIOAL layer of a different viscosity than the ribbon-shaped sample stream, such as a higher viscosity, advantageously tends to align non-spherical particles in a plane substantially parallel to the flow direction and exert forces on the cells, improving the focusing of content of the cells' intracellular structures. The optical axis of the high-resolution imaging device 24 is substantially perpendicular (i.e., orthogonal) to the plane of the ribbon-shaped sample stream 32. The linear velocity of the ribbon-shaped sample stream 32 at the imaging point may be, for example, 20 to 200 mm / s. In some embodiments, the linear velocity of the ribbon-shaped sample stream may be, for example, 50 to 150 mm / sec.

[0051] The thickness of the ribbon-shaped sample stream can be affected by the relative viscosities and flow rates of the sample fluid and PIOAL. Referring back to FIG. 1 , sample source 25 and / or PIOAL source 27, e.g., comprising a precision displacement pump and / or optimized flow restrictor tubing dimensions along with a single fluid source for driving the associated fluid flows, can be configured to provide sample and / or PIOAL at a controllable and optimized flow rate to optimize the dimensions of ribbon-shaped sample stream 32, i.e., as a thin ribbon at least as wide as the field of view of high optical resolution imaging device 24. Further information regarding approaches to sample driving that may be utilized in some embodiments is provided in International Patent Application No. PCT / US2022 / 054240, the disclosure of which is incorporated by reference in its entirety. In one example, the PIOAL is contained within a single tank with two flow paths - a first flow path that delivers the PIOAL to the flow cell, and a second flow path that delivers the PIOAL proximate to the specimen sample entry point near the flow cell, where the PIOAL is then used to push the specimen sample through the flow cell. Flow restrictors are configured on each flow path to affect the relative velocity / flow rates in each flow path, and the use of a single PIOAL source ensures that the velocity / flow ratio between the sample flow and the PIOAL flow is relatively constant.

[0052] In one embodiment, the PIOAL source 27 is configured to provide the PIOAL at a predetermined viscosity, which may be different from or higher than the viscosity of the sample. The viscosity and density of the PIOAL, the viscosity of the sample material, the flow rate of the PIOAL, and the flow rate of the sample material are adjusted to maintain a ribbon-shaped sample stream at a displacement distance from the autofocus pattern and with predetermined dimensional characteristics, such as a favorable ribbon-shaped sample stream thickness. In a further embodiment, the PIOAL may have a higher linear velocity and a higher viscosity than the sample, thereby elongating the sample into a flat ribbon. In some cases, the PIOAL viscosity may be up to 10 centipoise.

[0053] In the embodiment shown in FIG. 1C, the same digital processor 18 used to analyze pixelated digital images obtained from the photosensor array may also be used to control the autofocus motor 54. Typically, however, the high-light-resolution imaging device 24 is not autofocused for every image captured. The autofocus process may be performed periodically (at the start of the day or shift), or when, for example, a temperature or other process change is detected by an appropriate sensor, or when image analysis detects a potential need for refocusing. In some cases, the automated autofocus process may be performed within a period of about 10 seconds. In some cases, the autofocus procedure may be performed before processing a rack of samples (e.g., 10 samples per rack). In other embodiments, it is possible to have the blood image analysis performed by one processor, and a separate processor, optionally associated with its own photosensor array, be positioned to handle the autofocus step relative to the fixed target 44.

[0054] The digital processor 18 may be configured to autofocus at programmed times, under programmed conditions, or upon user request, as well as to perform image-based categorization and subcategorization of particles. Exemplary particles include cells, white blood cells, red blood cells, and the like. In one embodiment, the digital processor 18 is configured to detect an autofocus restart signal. The autofocus restart signal may be triggered by a detected temperature change, a deterioration in focus quality as recognized by pixel image date parameters, the passage of time, or user input. Advantageously, recalibration is not required in the sense of measuring the displacement distance 52 shown in FIG. 1 to recalibrate. Optionally, the autofocus may be programmed to recalibrate between runs at a specific frequency / interval for quality control and / or to maintain focus.

[0055] Displacement distance 52 varies slightly from flow cell to flow cell but remains constant for a given flow cell. As a setup process when attaching the image analyzer to the flow cell, the displacement distance is first estimated, and then during a calibration step in which autofocus and imaging aspects are performed, the exact displacement distance for the flow cell is determined and entered as a constant into the programming of processor 18. In further embodiments, processor 18 may present various information on display 63 for a user to review and / or analyze, as discussed further herein.

[0056] As noted above, some systems may include an imaging system / module having a flow cell 22, a high-optical resolution imaging device 24, and a processor 18, which, in conjunction with each other and other suitable components, are configured to utilize a sump fluid (e.g., a patient sample) to cooperatively (A) collect quality images of microparticles in the sample flow stream 32 using digital image processing, (B) record such collected images, and (C) process the collected digital images (e.g., categorize such microparticles into various appropriate categories and / or subcategories) using suitable data processing techniques that will be apparent to those skilled in the art in view of the teachings herein. In other words, an imaging system / module similar to that described above may be utilized to obtain information about the sample fluid through high-quality images of microparticles within the sample fluid. For example, static or slide-based imaging may be used in place of the flow imaging and flow cell imaging-based concepts described above and herein.

[0057] Imaging system combined with alternative systems In addition to the imaging-based systems and modules described herein, some systems / modules may obtain information from the sample fluid by means other than capturing high-quality images of microparticles in the sample flow stream 32. Such systems / modules may utilize, for example, impedance systems, fluorescence systems, light scattering systems, VCS systems (volume, conductivity, and scattering integration), spectrophotometry systems, or any other suitable systems that would be apparent to one of ordinary skill in the art in view of the teachings herein. Such systems may be referred to as alternative or “non-imaging” systems because they may not capture high-quality images of microparticles. Some alternative systems may include systems that utilize different imaging analysis processes (e.g., different from the flow imaging described herein) to obtain data. Alternative systems may collect sample fluid information that includes the same, similar, and / or different parameters compared to the information obtained by the imaging systems described above.

[0058] These alternative systems may be useful for obtaining specific particle information that may be difficult to derive from images. For example, the imaging system may not be able to evaluate cell-related volume data, and therefore an alternative system may need to be included with the imaging system to establish this volume data. In another example, the imaging system may not be able to evaluate hemoglobin content from images, and therefore a separate hemoglobin module (e.g., a spectrophotometer) is included as an additional module. These alternative systems may also be used to provide a second set of parameters for result verification (e.g., counting red blood cells using an imaging-based analysis system and a non-imaging-based analysis system).

[0059] In some embodiments, the analyzer or analytical system will utilize multiple channels—a first imaging channel (e.g., flow imaging) and one or more non-imaging channels (e.g., one or more of impedance, fluorescence, spectrophotometry, conductivity, light scatter, or volume-conductivity-scatter (VCS)). Each channel can also be thought of as a module, such that there is an imaging module and one or more non-imaging modules. In one example, the analyzer or analytical system utilizes a flow-imaging channel / module, an impedance channel / module, and a spectrophotometry channel / module.

[0060] In some embodiments, the second non-imaging channel can utilize multiple non-imaging modules therein (e.g., a combination of impedance, conductivity, light scattering, VCS, fluorescence, and spectrophotometry). In other words, there is a dedicated imaging channel and a dedicated non-imaging channel where all non-imaging analyses are performed on a particular channel. In one example, an analyzer or analysis system utilizes two channels—a first flow imaging channel and a second non-imaging channel that utilizes multiple non-imaging modules, including, for example, impedance and spectrophotometry modules. Further description of these alternative or non-imaging modules, channels, or systems is provided herein.

[0061] Impedance System Referring now to FIG. 2 , a schematic diagram of a cellular analysis system 200 is shown. In some embodiments, and as shown, the system 200 may include a preparation system 210, a transducer module 220, and an analysis system 230. While the system 200 is described herein at a very high level with reference to the three core system blocks (e.g., 210, 220, and 230), those skilled in the art will readily understand that the system 200 includes many other system components (as discussed above with reference to FIGS. 1 , 1B, and 1C ), such as a central control processor, a display system, a fluidics system, a temperature control system, a user safety control system, and the like. During operation, a fluid sample (e.g., a whole blood sample (WBS)) 240 may be presented to the system 200 for analysis. In some cases, the sample 240 is aspirated into the system 200. Exemplary aspiration techniques are known to those skilled in the art. After aspiration, the sample 240 may be delivered to the preparation system 210. The preparation system 210 can receive the sample 240 and perform operations related to preparing the sample 240 for further measurement and analysis. For example, the preparation system 210 can separate the sample 240 into predefined aliquots for presentation to the transducer module 220. The preparation system 210 can include a mixing chamber so that appropriate reagents can be added to the aliquots. For example, if the aliquot is to be tested for white blood cell subset population differentiation, a lytic reagent (e.g., ERYTHROLYSE, red blood cell lysis buffer) can be added to the aliquot to destroy and remove red blood cells (RBCs). The preparation system 210 can include temperature control components (not shown) to control the temperature of the reagents and / or the mixing chamber. Proper temperature control can improve the consistency of the operation of the preparation system 210.As discussed elsewhere herein, sample data such as light scatter data, light absorption data, and / or current data may be obtained (e.g., using a transducer) and processed or used to determine various blood cell status indications for an individual patient.

[0062] In some cases, predefined aliquots may be transferred from preparation system 210 to transducer module 220. As described in further detail below, transducer module 220 may be capable of performing cellular direct current (DC) impedance, radio frequency (RF) conductivity, light transmission, and / or light scatter measurements from samples 240 individually passing through transducer module 220. Measured DC impedance, RF conductivity, and light propagation (e.g., light transmission, light scatter) parameters may be provided or transmitted to analysis system 230 for data processing. In some cases, analysis system 230 may include computer processing features and / or one or more modules or components, such as those described herein with reference to the system shown in FIG. 9 and described further below, that can evaluate the measured parameters, identify and enumerate blood cell constituents, and correlate a subset of data characterizing the elements of sample 240 to an individual's white blood cell count (WBC) status. As shown here, the cellular analysis system 200 may generate or output a report 250 containing a predicted condition and / or prescribed treatment regimen for the individual. In some cases, excess biological sample from the transducer module 220 may be directed to an external (or alternatively, internal) waste system 260.

[0063] In one embodiment, the transducer module 220 includes an impedance detector that utilizes impedance, also known as the Coulter principle, to count individual cells as they pass through an aperture (correlating the displacement and corresponding electrical response to cell size / volume). In one embodiment, the impedance detector is configured to measure one or more of red blood cells, white blood cells, and platelets. In one embodiment, the impedance detector is configured to measure red blood cells and platelets (e.g., configuring a threshold to only count cells within the range of blood cells and platelets), mean corpuscular volume (average volume of red blood cells), and mean platelet volume (average volume of platelets).

[0064] In the context of Figure 3, which shows the transducer module in more detail (and references the impedance portion of the transducer module), there are electrodes 334, 336 for performing DC impedance measurements of cells passing through an interrogation zone (e.g., two tanks separated by an aperture through which the cells pass). Signals from electrodes 334, 336 are sent to analysis system 304, which processes the data and establishes cell counts and other numerical cell parameters (e.g., volume data). This data is then output to report 306. Any remaining fluid is discharged to waste system 308.

[0065] In one example, the use of an impedance detector alone may have particular utility for counting red blood cells and platelets, or similarly for counting white blood cells where differentiation between various types of white blood cells is not required. This is because it may be difficult to distinguish between various types of white blood cells (e.g., at least neutrophils, lymphocytes, monocytes, eosinophils, and basophils) through impedance measurements alone, which would count white blood cells and assess their size but require further analysis to identify the type of white blood cell. By way of example, an impedance detector may be used in connection with one or more of red blood cell counting, platelet counting, mean corpuscular volume, mean platelet volume, and / or white blood cell counting.

[0066] Conductivity System FIG. 3 shows the transducer module in more detail and associated components, including conductivity measurements. Note that FIG. 3 illustrates how impedance (DC) measurements and conductivity can be integrated into a single system. In some embodiments, and as shown, system 300 may include a transducer module 310 with a flow cell 330, which may include an electrode assembly with a first electrode 334 and a second electrode 336 for performing DC impedance and RF conductivity measurements of cells passing through a cell interrogation zone 332. Signals from electrodes 334, 336 may be transmitted to analysis system 304. The electrode assembly can analyze the volume and conductivity characteristics of cells using low-frequency and high-frequency currents, respectively. For example, low-frequency DC impedance measurements can be used to analyze the volume of each individual cell passing through the cell interrogation zone. Relatedly, high-frequency RF current measurements can be used to determine the conductivity of cells passing through the cell interrogation zone. Because the cell wall acts as a conductor for radiofrequency current, radiofrequency current can be used to detect differences in the insulating properties of cellular components because the current passes through the cell wall and into the interior of each cell. Radiofrequency current can be used to characterize the nuclear and granular constituents and chemical composition inside the cell.

[0067] Wires or other transmission or connectivity mechanisms can transmit signals from the electrode assembly (e.g., electrodes 334, 336) to the analysis system 304 for processing. For example, measured DC impedance or RF conductivity parameters can be provided or transmitted to the analysis system 304 for data processing. In some cases, the analysis system 304 may include computer processing features and / or one or more modules or components, such as those described herein with reference to the system shown in FIG. 9, that can evaluate the measured parameters, identify and enumerate biological sample constituents, and correlate subsets of data characterizing the elements of the biological sample to the individual's condition. As shown here, the cellular analysis system 300 may generate or output a report 306 that includes a predicted condition and / or prescribed treatment regimen for the individual. In some cases, excess biological sample from the transducer module 310 may be directed to an external (or, alternatively, internal) waste system 308. In some cases, the cellular analysis system 300 may include one or more features of a transducer module or blood analysis instrument, such as those described in previously incorporated U.S. Patent Nos. 5,125,737; 6,228,652; 8,094,299; and 8,189,187.

[0068] In some embodiments, the conductivity system can be standalone (e.g., would not include an impedance detector) or may be paired with an impedance detector to provide additional particle information.

[0069] Light Scattering System FIG. 4 illustrates an embodiment of an automated cellular analysis system for predicting or assessing white blood cell (WBC) type. In particular, WBCs can be assessed based on a biological sample obtained from an individual's blood. As shown here, an analysis system or transducer 400 may include an optical element 410 having a cell interrogation zone 412. The transducer also provides a flow path 420 that delivers a hydrodynamically focused stream 422 of the biological sample toward the cell interrogation zone 412. For example, as the sample stream 422 is launched toward the cell interrogation zone 412, a volume of sheath fluid 424 can enter the optical element 410 under pressure, uniformly surrounding the sample stream 422 and forcing the sample stream 422 to flow through the center of the cell interrogation zone 412, thus achieving hydrodynamic focusing of the sample stream. In this manner, individual cells of the biological sample can be precisely analyzed, one cell at a time, as they pass through the cell interrogation zone.

[0070] For purposes of illustration in the context of FIG. 4 , note that light scattering analysis has been combined with direct current (DC) impedance and radio frequency (RF) conductivity in a single module or system 400. The transducer module or system 400 also includes an electrode assembly 430 that measures the direct current (DC) impedance and radio frequency (RF) conductivity of cells 10 of a biological sample that individually pass through the cell interrogation zone 412. The electrode assembly 430 may include a first electrode mechanism 432 and a second electrode mechanism 434. As discussed elsewhere herein, low-frequency DC measurements can be used to analyze the volume of each individual cell that individually passes through the cell interrogation zone. Relatedly, high-frequency RF current measurements can be used to determine the conductivity of cells passing through the cell interrogation zone. Such conductivity measurements can provide information about the internal cellular content of the cells. For example, high-frequency RF current can be used to analyze the nuclear and granular constituents and the internal chemical composition of individual cells that pass through the cell interrogation zone. Thus, in some embodiments, DC and RF measurements may be made on cells passing through the cell interrogation zone. As explained above, for purposes of illustration, light scatter has been combined with DC and RF measurements in a single module or system 400. This may be desirable in some contexts to provide additional cellular information (e.g., all non-imaging-based data) in one simplified structure. Alternative embodiments can have light scatter alone (i.e., no impedance or conductivity) or can include a combination of impedance and conductivity as a separate module added to the light scatter module. The principles of light scatter detection will now be further explained.

[0071] 12, as shown therein, a cellular analysis system may include a transducer module 2910 having a light source or illumination source, such as a laser 2912, that emits a beam 2914. Laser 2912 can be, for example, a 635 nm, 5 mW, solid-state laser. In some cases, system 2900 may include a focusing system 2920 that adjusts beam 2914 so that resulting beam 2922 is focused and positioned at a cell interrogation zone 2932 of flow cell 2930. In some cases, flow cell 2930 receives a sample aliquot from preparation system 2902. As explained above, a light scattering detection system is also illustratively shown along with DC (impedance) and RF (conductivity), but it should be noted that this can be a stand-alone system or module.

[0072] In some cases, the aliquots generally flow through cell interrogation zone 2932 such that its constituents pass through cell interrogation zone 2932 one at a time. In some cases, system 2900 may include a cell interrogation zone or transducer module or other features of a hematology analyzer, such as those described in U.S. Patent Nos. 5,125,737, 6,228,652, 7,390,662, 8,094,299, and 8,189,187, the contents of each of which are incorporated herein by reference in their entirety. For example, cell interrogation zone 2932 may be defined by a rectangular cross-sectional measurement of approximately 50 x 50 microns and have a length (measured in the direction of flow) of approximately 65 microns. The flow cell 2930 may include an electrode assembly having a first electrode 2934 and a second electrode 2936 for performing DC impedance and RF conductivity measurements of cells passing through the cell interrogation zone 2932. Signals from the electrodes 2934, 2936 may be transmitted to the analysis system 2904. The electrode assembly can analyze the volume and conductivity properties of cells using low-frequency and high-frequency currents, respectively. For example, low-frequency DC impedance measurements can be used to analyze the volume of each individual cell passing through the cell interrogation zone. Relatedly, high-frequency RF current measurements can be used to determine the conductivity of cells passing through the cell interrogation zone. Because cell walls act as conductors for high-frequency current, high-frequency current can be used to detect differences in the insulating properties of cellular components as the current passes through the cell wall and into the interior of each cell. High-frequency current can be used to characterize the nuclear and granular constituents and chemical composition within the cells.

[0073] Incident beam 2922 travels along beam axis AX and illuminates cells passing through cell interrogation zone 2932, resulting in light propagation (e.g., scatter, transmission) within angular range a emanating from zone 2932. Exemplary systems are equipped with a sensor assembly capable of detecting light within three, four, five, or more angular ranges within angular range a, including light associated with extinction or axial light loss measures described elsewhere herein. As shown here, light propagation 2940 can be detected by a light detector assembly 2950, ​​optionally having a light scatter detector unit 2950A and a light scatter and transmission detector unit 2950B. In some cases, light scatter detector unit 2950A includes a photoactive region or sensor zone for detecting and measuring upper median angle light scatter (UMALS), e.g., light scattered or otherwise propagated at angles within a range of about 20 degrees to about 42 degrees relative to the light beam axis. In some instances, UMALS corresponds to light propagating within an angular range of about 20 degrees to about 43 degrees relative to the incident beam axis illuminating cells flowing through the interrogation zone. The light scatter detector unit 2950A may include a photoactive region or sensor zone for detecting and measuring lower median angle light scatter (LMALS), for example, light scattered or otherwise propagated at an angle within a range of about 10 degrees to about 20 degrees relative to the light beam axis. In some instances, LMALS corresponds to light propagating within an angular range of about 9 degrees to about 19 degrees relative to the incident beam axis illuminating cells flowing through the interrogation zone.

[0074] The combination of UMALS and LMALS is defined as median angle light scatter (MALS), which is light scattering or propagation at angles between about 9 degrees and about 43 degrees relative to the incident beam axis illuminating cells flowing through the interrogation zone.

[0075] 12 , light scatter detector unit 2950A may include an opening 2951 that allows low-angle light scatter or propagation 2940 to pass beyond light scatter detector unit 2950A and thereby reach and be detected by light scatter and transmission detector unit 2950B. According to some embodiments, light scatter and transmission detector unit 2950B may include a photoactive region or sensor zone for detecting and measuring lower angle light scatter (LALS), e.g., light scattered or propagated at an angle of about 5.1 degrees relative to the illuminating light beam axis. In some cases, LALS corresponds to light propagating at an angle of less than about 9 degrees relative to the incident beam axis that illuminates cells flowing through the interrogation zone. In some cases, LALS corresponds to light propagated at an angle of less than about 10 degrees relative to the incident beam axis that illuminates cells flowing through the interrogation zone. In some cases, LALS corresponds to light propagated at an angle of about 1.9 degrees ± 0.5 degrees relative to the incident beam axis illuminating cells flowing through the interrogation zone. In some cases, LALS corresponds to light propagated at an angle of about 3.0 degrees ± 0.5 degrees relative to the incident beam axis illuminating cells flowing through the interrogation zone. In some cases, LALS corresponds to light propagated at an angle of about 3.7 degrees ± 0.5 degrees relative to the incident beam axis illuminating cells flowing through the interrogation zone. In some cases, LALS corresponds to light propagated at an angle of about 5.1 degrees ± 0.5 degrees relative to the incident beam axis illuminating cells flowing through the interrogation zone. In some cases, LALS corresponds to light propagated at an angle of about 7.0 degrees ± 0.5 degrees relative to the incident beam axis illuminating cells flowing through the interrogation zone.

[0076] According to some embodiments, the light scatter and transmission detector unit 2950B may include a photoactive region or sensor zone for detecting and measuring light transmitted axially through a cell or propagated from a cell illuminated at a 0-degree angle relative to the incident light beam axis. In some cases, the photoactive region or sensor zone may detect and measure light propagated axially from a cell at an angle of less than about 1 degree relative to the incident light beam axis. In some cases, the photoactive region or sensor zone may detect and measure light propagated axially from a cell at an angle of less than about 0.5 degrees relative to the incident light beam axis. Such axially transmitted or propagated light measurements correspond to axial light loss (ALL, or AL2). As discussed in previously incorporated U.S. Patent No. 7,390,662, when light interacts with a particle, some of the incident light changes direction through a scattering process (i.e., light scattering), and some of the light is absorbed by the particle. Both of these processes remove energy from the incident beam. When viewed along the beam's axis of incidence, light loss can be referred to as forward extinction or axial light loss. Further aspects of axial light loss measurement techniques are described in U.S. Pat. No. 7,390,662, column 5, line 58 to column 6, line 4.

[0077] Thus, the cellular analysis system 2900 provides a means for obtaining light propagation measurements, including light scatter and / or light transmission, for light emanating from illuminated cells of a biological sample at any of a variety of angles or within any of a variety of angular ranges, including ALL and multiple distinct light scatter or propagation angles. For example, a photodetector assembly 2950, ​​including appropriate circuitry and / or processing units, provides a means for detecting and measuring UMALS, LMALS, LALS, MALS, and ALL.

[0078] Wires or other transmission or connectivity mechanisms can transmit signals from the electrode assembly (e.g., electrodes 2934, 2936), light scatter detector unit 2950A, and / or light scatter and transmission detector unit 2950B to analysis system 2904 for processing. For example, measured DC impedance, RF conductivity, light transmission, and / or light scatter parameters may be provided or transmitted to analysis system 2904 for data processing. In some cases, analysis system 2904 may include computer processing features and / or one or more modules or components, such as those described herein, that can evaluate the measured parameters, identify and enumerate biological sample constituents, and correlate a subset of data characterizing the elements of the biological sample to the infection status of the individual. As shown here, cellular analysis system 2900 may generate or output a report 2906 containing the assessed infection status and / or prescribed treatment regimen for the individual. In some cases, excess biological sample from the transducer module 2910 may be directed to an external (or alternatively internal) waste system 2908. In some cases, the cellular analysis system 2900 may include one or more features of transducer modules or blood analysis instruments such as those described in previously incorporated U.S. Patent Nos. 5,125,737, 6,228,652, 8,094,299, and 8,189,187.

[0079] Fluorescence System FIG. 10 shows an illustrative flow cytometer 2000 that may be utilized in a fluorescence system to measure various parameters of a sample fluid, as will be apparent to those skilled in the art in view of the teachings herein. In some cases, cells from a hematological sample are treated with a hemolyzing agent to lyse erythrocytes, thereby leaving white blood cells in the sample fluid. Furthermore, the remaining white blood cells may then be stained with a fluorescent dye that can produce a differential fluorescence intensity. Such a preparation procedure may utilize the teachings of the sample preparation process described herein. Once the white blood cells have been appropriately stained according to the description herein, the sample fluid containing the stained cells may be introduced into the flow cytometer 2000, and the scattered light and fluorescence of each cell may be measured as the cells are illuminated by a laser.

[0080] The flow cytometer 2000 includes a light source 2021 (e.g., a red semiconductor laser) configured to emit a light beam (e.g., a laser beam having a wavelength of 633 nm) into the orifice of a sheath flow cell 2023 through a collimating lens 2022. Simultaneously, particles (e.g., cells—such as blood cells or body fluid cells) from the sample fluid individually pass through a nozzle 2020 and enter the orifice of the sheath flow cell 2023. Thus, the particles are directed into the sheath fluid and are configured to pass through the light beam emitted from the light source 2021 within the sheath flow cell 2023. The light source 2021 illuminates the orifice of the flow cell, into which the prepared measurement sample has been introduced, with light that can excite the dye used in sample treatment and is selected depending on the fluorescent dye that stains the particles (e.g., blood cells or body fluid cells) in the sample. Therefore, in addition to the semiconductor laser, for example, a red argon laser, a He-Ne laser, or a blue semiconductor laser may be used depending on the type of fluorescent dye used.

[0081] Forward scattered light emitted from the particles is introduced into a forward scattered light detector 2026 (e.g., a photodiode) via a collecting lens 2024 and a pinhole plate 2025. Furthermore, side scattered light emitted from the particles is introduced into a side scattered light detector 2029 (e.g., a photomultiplier tube) via a collecting lens 2027 and a dichroic mirror 2028. Side fluorescent light emitted from the particles is also introduced into a side fluorescent light detector 2031 (e.g., a photomultiplier tube) via a collecting lens 2027, a dichroic mirror 2028, a filter 2028′, and a pinhole plate 2030. The forward scattered light signal output from the forward scattered light detector 2026, the side scattered light signal output from the side scattered light detector 2029, and the side fluorescent light signal output from the side fluorescent light detector 2031 are amplified by amplifiers 2032, 2033, and 2034, respectively, and input to the controller 2006. The controller 2006 analyzes these signals and calculates the received signal strength. The controller 2006 or any other suitable component of the fluorescence system may utilize these scattered light intensities to calculate and display appropriate measured parameters, as will be apparent to those skilled in the art in view of the teachings herein. Further information regarding fluorescence systems that may be applied in some embodiments for cell analysis is provided in U.S. Pat. Nos. 7,625,730 and 7,892,841, the disclosures of each of which are incorporated by reference in their entireties.

[0082] It should be noted that fluorescence systems are sometimes referred to in the art as optical systems because they leverage the use of mirrors in laser excitation and non-imaging configurations, and therefore fluorescence systems may also be referred to as optical systems.

[0083] Some fluorescence techniques may also leverage imaging as part of the analytical process (e.g., fluorescence in situ hybridization, a.k.a. FISH). A fluorescence imaging module (e.g., FISH) may be used as part of an additional module used to evaluate a biological sample (e.g., blood cells) as a different module from the previously described flow imaging module. In this context, the use of fluorescence may be applied to imaging or non-imaging systems or modules, as appropriate. For example, a multi-module analytical system may include a flow imaging module (e.g., FIG. 1) and a fluorescence imaging module as separate imaging modules. Alternatively, a multi-module analytical system may include a flow imaging module (e.g., FIG. 1) and a separate fluorescence module that may include a fluorescence imaging component. Alternatively, a multi-module analytical system may include an imaging module (e.g., flow imaging or FISH in FIG. 1) and at least one separate module that does not utilize imaging (e.g., impedance, spectrophotometry, fluorescence cytometry, light scattering, or conductivity).

[0084] Spectrophotometer System 13 shows a spectrophotometer 3000 operable to measure absorption, transmittance, and / or other properties of a diluted, lysed blood sample and used to measure red blood cell hemoglobin content—in one example, hemoglobin concentration—for the blood sample. The measured properties are then converted to corresponding measurements for hematological parameters.

[0085] The spectrophotometer includes a light source 3021a, a lens 3021b, a prism 3021c, a cuvette 3021d, and a detector 3021e. To arrive at an absorption or transmittance reading, a blood sample is passed through the cuvette, and the light source emits light through the lens 3021b, the prism 3021c, the cuvette 3021d, and the passing blood sample. A detector 3021e, positioned on the opposite side of the cuvette 3021d, obtains an absorption and / or transmittance reading for the blood sample. To convert the absorption and / or transmittance reading for the blood sample to a hematology measurement, a lookup table may be used to correlate the reading to the hematology measurement, or alternatively, the system is programmed to perform this calculation. This is accomplished by the processor 3024 and memory 3025.

[0086] In the embodiment of FIG. 13 , processor 3024 and memory 3025 are included as part of an automated hematology analyzer. However, processor 3024 and memory 3025 may take a number of different forms, such as a processor in a connected personal computer or other device operable to convert absorption and / or transmittance readings into uncorrected hematology measurements, such as hemoglobin concentration. In this embodiment, processor 3024 may be any commercially available microprocessor. Processor 3024, in conjunction with memory 3025, is further operable to take and convert the uncorrected hematology measurements into corrected hematology parameters, which are based on the uncorrected hematology measurements and temperature measurements taken by temperature sensor 3017. The corrected hematology measurements compensate for inaccuracies in the uncorrected hematology measurements due to temperature, providing more accurate measurements of the hematology parameters measured in the blood sample.

[0087] FIG. 14 shows that a blood sample is obtained 3102, diluted and lysed 3104, and passed through a cuvette 3021d in step 3108. As described above, in one embodiment, the cuvette 3021d is part of a spectrophotometer 3000 or other measurement device. The spectrophotometer 3000 obtains absorption and / or transmittance measurements on the blood sample in step 3110. These measurements are then passed to a processor 3024 in step 3112, and a hemoglobin measurement is determined by the processor 3024 based on the absorption / transmittance measurements. In one embodiment, the processor 3024 determines the hemoglobin measurement using a lookup table stored in memory 3025, or is programmed to correlate the absorption / transmittance measurements to the hemoglobin measurement. In particular, to arrive at the hemoglobin measurement, the processor 3024 simply uses the absorption measurements obtained on the blood sample to arrive at the corresponding hemoglobin measurement. The processor may then obtain a hemoglobin measurement in step 3112.

[0088] System Implementation 15 shows an example of an imaging system and a non-imaging system combined into a single test apparatus 4000. The test apparatus 4000 may include a Sample Aspiration Module (SAM), an imaging system 4200, and a non-imaging system 4100 (e.g., an impedance system, a conductivity system, a light scattering system, or a fluorescence system). The SAM may include a probe 4005 and an aspiration pump 4010. The imaging system 4200 and the non-imaging system 4100 may be in fluid communication with the SAM, thereby enabling the SAM to provide a fluid sample to the imaging system 4200 and the non-imaging system 4100. In other words, the imaging system 4200 receives one portion (e.g., a first portion) of the blood sample, and the non-imaging system 4100 receives another portion (e.g., a second portion) of the blood sample (e.g., two different aliquots of the same blood sample or an aliquot of the same blood sample is split into a first portion that enters the imaging system 4200 and a second portion that enters the non-imaging system 4100).

[0089] 17 shows a detailed example of an imaging system 4200 of the test device 4000. The imaging system 4200 may include an RBC chamber 4215, a first WBC chamber 4220, a second WBC chamber 4225, an imaging component 4230 having a flow cell 4233, a stain 4235, a diluent 4240, a sheath 4245, and a waste container 4250. This is for illustrative purposes only; any combination of RBC and WBC chambers can be present. The blood is separated into the RBC and WBC chambers, with the blood in the WBC chamber receiving additional reagents and preparations, as described herein.

[0090] The probe 4005 may be used to mix various fluid samples prior to use. Once mixed, the sample may be aspirated using negative pressure at the probe 4005 from the suction pump 4010, and the probe may then be positioned sequentially in both the RBC chamber 4215 and the WBC chambers 4220, 4225, thereby delivering a first portion of the blood sample to the RBC chamber 4215 and the WBC chambers 4220, 4225. In one embodiment, the RBC chamber 4215 is configured to receive a diluent, while the WBC chambers 4220, 4225 are configured to receive a diluent, a lysing reagent (to lyse / remove red blood cells), and a staining reagent (to stain the nuclear region of white blood cells). The divided blood sample in the WBC chambers 4220, 4225 may then be mixed with a staining agent 4235 and a diluent 4240 and incubated in the chambers 4215, 4220, 4225 using integrated heaters. Due to the difficulty in distinguishing white blood cells, it is helpful to stain the nuclear region to better illustrate and display the nuclear region to aid in white blood cell differentiation (e.g., distinguishing at least neutrophils, lymphocytes, monocytes, eosinophils, and basophils). Lysis is used to eliminate red blood cells during this white blood cell analysis cycle.

[0091] In one embodiment, the staining and lysis reagents are two separate compounds added during separate deposition steps. In one embodiment, the staining and lysis reagents are in a single composition that includes both the staining agent and the lysis agent—the composition includes saponin, multiple staining agents (e.g., a combination of new methylene blue, crystal violet, and basic fuchsin), and gluteraldehyde. Further information regarding staining and lysis compounds can be found in U.S. Pat. No. 9,279,750 and U.S. Published Patent Application No. 2021 / 0108994, the disclosures of each of which are incorporated herein by reference in their entireties.

[0092] Once cultured, the blood may be pumped into a flow cell (e.g., 22 in FIG. 1) within the imaging component 4230. Blood from the RBC chamber 4215 is imaged in one cycle. Note that this cycle takes less time because the RBC chamber does not receive staining and lysing reagents. Blood from the WBC chambers 4220, 4225 is imaged in a different cycle (e.g., two separate cycles). Once in the flow cell 4233 and within the stream of the sheath 4245, the Optical Bench Module (OBM) may capture cell images and convert the full frames into patches. After conversion, the Image Processing Module (IPM) may preprocess and classify the patches. The classified patches may then be used to generate analytical data.

[0093] The sample portions remaining in chambers 4215, 4220, 4225 may then flow from their respective chambers to an alternate system (not shown) where further measurements can be performed on the sample (e.g., for a different analytical test). Alternatively, any sample portions remaining in chambers 4215, 4220, 4225 may be flushed into waste container 4250, and the chambers may be washed (e.g., with a diluent) in anticipation of receiving another blood sample. The portion of the analyte already analyzed through imaging component 4230 (and optionally in an alternate system after the imaging step) may then be deposited in waste container 4250, and imaging system 4200 may be washed (e.g., with a diluent) in preparation for a subsequent blood sample.

[0094] The inclusion of non-imaging system 4100 can be useful for a variety of reasons, including providing a secondary source of information using more traditional blood analysis techniques to confirm results or providing analysis for cellular parameters that may be difficult to assess by imaging—e.g., volumetric data such as mean corpuscular volume (MCV) or hemoglobin content of red blood cells. In some embodiments, system 4100 can be an alternative system that is not a non-imaging system but rather performs auxiliary imaging in another manner as a further step to the flow imaging system of imaging system 4200. In various examples, the non-imaging system can include various combinations of impedance, conductivity, light scatter, volume-conductivity-scatter (VCS), fluorescence, and spectrophotometry modules.

[0095] 16 illustrates a non-imaging system 4100 that includes, among other components, a pair of fluid analysis chambers including a first fluid analysis chamber in the form of a first bath 2212 and a second fluid analysis chamber in the form of a second bath 2214. The first bath 2212 is a white blood cell (WBC) or hemoglobin (HGB) bath, and the second bath 2214 is a red blood cell (RBC) bath. In the example shown, the WBC bath 2212 opens to allow a sample probe 4005 of the testing device 4000 to selectively access the WBC bath 2212, e.g., to aspirate fluid from and / or dispense fluid into the WBC bath 2212. Although not shown, the WBC and RBC baths 2212, 2214 of this embodiment may be contained within the confines of the non-imaging system 4100. The non-imaging system 4100 also includes a sweep tank 2241 in selective fluid communication with both buses 2212, 2214. The non-imaging system 4100 also includes a plurality of fluid reservoirs including a first fluid reservoir in the form of a diluent reservoir 2230 containing a diluent (D), a second fluid reservoir in the form of a lysing agent reservoir 2232 containing a lysing agent (L), and a third fluid reservoir in the form of a cleaning agent reservoir 2233 containing a cleaning agent (CL).

[0096] The diluent reservoir 2230 is in fluid communication with the sweep flow tank 2241, the WBC bus 2212, and the RBC bus 2214. Additionally, the wash reservoir 2233 is in fluid communication with the sweep flow tank 2241, the buses 2212, 2214, and any other suitable components, as would be apparent to one of ordinary skill in the art in view of the teachings herein. The non-imaging system 4100 may deliver a diluent (D) from the diluent reservoir 2230 to the sweep flow tank 2241, the WBC bus 2212, and the RBC bus 2214 to appropriately dilute the sample in accordance with the description herein. In some cases, the sweep flow tank 2241 may selectively receive the diluent (D) and the wash (CL) in accordance with the description herein and similarly communicate such received fluids to the buses 2212, 2214. It should also be understood that the buses 2212, 2214 may be in fluid communication with the reservoirs 2230, 2233 such that the buses 2212, 2214 may directly receive the diluent (D) and cleaning agent (CL).

[0097] The non-imaging system 4100 is configured to be in suitable communication with cleaner (CL) baths 2212, 2214, sweep flow tank 2241, and various other suitable components of the non-imaging system, as would be apparent to one of ordinary skill in the art in view of the teachings herein. Cleaner (CL) may be distributed throughout the system 4100 to suitably remove traces of previous samples processed by the system 4100.

[0098] Additionally, the lysing agent reservoir 2232 is in fluid communication with the WBC bus 2212. The non-imaging system 4100 is configured to deliver a lysing agent (L) from the lysing agent reservoir 2232 into the WBC bus 2212 to properly lyse the blood sample to properly remove red blood cells from the sample in the WBC bus 2212.

[0099] The buses 2212, 2214 and / or sweep flow tank 2241 are also in suitable communication with the sample analyzer 2221 so that the sample fluid may be transferred to the sample analyzer 2221 for appropriate analysis, as would be apparent to one of ordinary skill in the art in view of the teachings herein. The waste receptacle 2246 is in fluid communication with various components of the system 4100 so that the processed sample, diluent (D), detergent (CL), lysing agent (L), etc. used in connection with the system 4100 may be properly disposed of after illustrative use.

[0100] The non-imaging system 4100 is configured to analyze a biological sample. In some embodiments, the non-imaging system 4100 is configured to analyze a blood sample, whereby the non-imaging system 4100 may be referred to as a blood analysis system. Although not shown, the WBC bus 2212 of this embodiment may include a hemoglobin transducer configured to measure the amount of hemoglobin present in the fluid medium contained in the WBC bus 2212. For example, the hemoglobin transducer may include a light source (e.g., a filtered light source) and an optical sensor configured to receive an optical signal emitted from the light source through the fluid medium contained in the WBC bus 2212. In some embodiments, the WBC and RBC buses 2212, 2214 may each be fluidly coupled to an appropriate sample analyzer 2221 via corresponding input and output conduits equipped with respective valves for selectively transporting fluid media from one of the WBC or RBC buses 2212, 2214 to the appropriate sample analyzer 2221 and / or for returning such fluid media from the sample analyzer 2221 to the WBC or RBC buses 2212, 2214. The sample analyzer 2221 may be configured to measure any suitable parameter (e.g., complete blood count, etc.) of the fluid media received from each bus 2212, 2214, as would be apparent to one of ordinary skill in the art in view of the teachings herein. In other embodiments, only one of the WBC or RBC buses 2212, 2214 (e.g., only the RBC bus 2214) may be fluidly coupled to the sample analyzer 2221. In the example shown, the sample analyzer 2221 is also fluidly coupled to a pneumatic transducer 2222. Although analysis of blood (e.g., impedance-based counting, optical techniques, and / or imaging) is shown and described herein, the biological analysis system 2210 may analyze (and optionally image) a variety of fluids, including, but not limited to, other bodily fluids such as synovial fluid, urine, bone marrow, etc.

[0101] It should be understood that the non-imaging system 4100 may include any other suitable components as would be apparent to one of ordinary skill in the art in view of the teachings herein. Accordingly, suitable fluid lines, pumps, valves, multi-flow units, etc. may be readily incorporated into the non-imaging system 4100.

[0102] Referring again to FIG. 5, in some embodiments, a blood sample may be received in a test tube and / or obtained for testing that includes an identifier 501. For example, in some embodiments, the blood sample or blood sample container may include a barcode 4057, a QR code, a radio frequency identification (RFID), or the like. The identifier may contain relevant details about the sample, such as, for example, patient information, temporary data associated with the sample, desired testing procedures, and the like. Thus, in some embodiments, the system may obtain the data contained in the identifier automatically or through user assistance and may determine one or more tests for the sample 502.

[0103] Once the test is determined 502, the system, in some embodiments, may capture 503 images of the blood cells in the flow cell. For example, a flow imaging system such as that shown in FIGS. 1, 1A, and 1B may be used to capture 503 images of the blood cells as they pass through the flow cell. In addition to image capture 503, the system may include an analysis system or transducer (e.g., 300 and 400) (e.g., alternative systems) that measures 504 the impedance of the blood cells. Other types of measurement channels or modules, such as fluorescence or spectrophotometry channels, may also be included. Using measurements from these various channels (e.g., captured images and measured impedance), data related to the sample may be derived 505. This derived data may then be displayed to a user or operator for evaluation 505. As a non-limiting example, Table 1, shown below, provides a non-exhaustive list of possible parameters that may be determined and / or derived for a sample using the disclosed techniques.

[0104] [Table 1]

[0105] For the purposes of illustration in Table 1, the majority of flow imaging-derived cellular data is associated with cell counts, and thus the data derived from the images is primarily counts. In other examples, quantitative data regarding individual cell types may be associated with flow imaging techniques, such as cell diameter or nuclear area of ​​individual cells.

[0106] In some embodiments, the aliquoter may be configured to separate the sample into multiple aliquots, whereby each aliquot may undergo a separate analysis (e.g., image-based or impedance-based). As such, it should be understood that the sample may be partitioned and passed to different modules for analysis, as discussed herein. For example, in some embodiments, the analysis system may be adapted to flow a first portion of the sample through a flow imaging module for red blood cell (RBC) imaging, while another portion of the sample is passed through a second flow cell for white blood cell (WBC) imaging.

[0107] Non-smear-based image analysis In further embodiments, and as shown in FIGS. 6 and 7 , the system may include a user interface that allows the user to evaluate possible outliers or errors in the analysis without the need for manual evaluation (e.g., smearing). In other words, the user can use images derived from on-screen flow imaging to confirm results without the need to perform separate imaging utilizing a smear / slide sample—thereby saving considerable time. Alternatively, the user can use the presented images to confirm that cells are correctly labeled and / or to confirm the presented results. In some cases, this type of functionality may be implemented using an algorithm that analyzes the captured images and / or associated data, such as impedance measurements, to identify issues that may require further review. An example of a method that may be implemented to allow the user to review such issues is shown in FIG. 6 . In the method shown in that figure, an image may first be captured by an image capture device 601 as it passes through the flow cell. Based on the captured 601 images, possibly as well as other types of data (e.g., impedance measurements, fluorescence measurements, etc.), a processor (e.g., 18) may be able to generate 602 result data including sample parameters (e.g., parameters shown in Table 1). The system may then analyze the captured images, possibly in combination with other data, to determine 603 review instructions to present to the user. This may be done, for example, using a machine learning algorithm such as that shown in FIG. 18, which was trained to classify images of particles from blood cells into various cell classifications, including normal and abnormal cells. Note that FIG. 18 is provided as an illustrative example of an architecture for a cell classifier used to label cells, and that various types of models for this purpose may be used, e.g., neural networks, convolutional neural networks, commonly available modified neural networks, etc.A further example may leverage pixel analysis and masking techniques to determine cell classification. Further information regarding techniques that some embodiments may use in cell classification can be found in U.S. Patent No. 11,403,751, the disclosure of which is incorporated by reference in its entirety.

[0108] In various embodiments, a single classifier is used to classify all cell types, including abnormal cell types. In some embodiments, multiple classifiers may be used with a voting protocol used to provide a final classification of cell types. In some embodiments, the multiple classifiers include classifiers that are specifically assigned to abnormal cell types or subsets of abnormal cell types (e.g., if a cell is classified as a red blood cell, it can trigger the use of a classifier that is specific to identifying abnormal cell types associated with red blood cells).

[0109] In the architecture of FIG. 18 , an input image 1801 is analyzed in a series of stages 1802 a- 1802 n, each of which may include one or more layers, and which are shown in more detail in FIG. 19 . As shown in FIG. 19 , an input 1901 (which may be a cell image in the initial layer 1902 a of FIG. 18 and may otherwise be the output of a previous stage) is provided to stage 1902, where it is processed by the convolutional layers of stage 1902 to generate one or more transformed images 1903 a- 1903 n. This processing may include convolving input 1901 with a set of filters 1904 a- 1904 n, each of which will identify a type of feature from the underlying image that will then be captured in that filter's corresponding transformed image. For example, as a simple example, convolving an image with the filters shown in Table 2 can generate transformed image capturing edges from input 1901.

[0110]

number

[0111] Table 2: Example convolution filters

[0112] As shown in FIG. 19, in addition to generating transformed images 1903a-1903n, a stage may include a pooling layer that generates pooled images 1905a-1905n for each of the transformed images 1903a-1903n. This may be done, for example, by organizing the appropriate transformed image into a set of regions and then replacing the values ​​within the region with a single value, such as the maximum value for the region or the average of the values ​​for the region. The result will be a pooled image whose resolution will be reduced relative to its corresponding transformed image based on the size of the regions into which the transformed image was divided (e.g., if the transformed images 1903a-1903n have N×N dimensions and are divided into 2×2 regions, the pooled images 1905a-1905n will have size (N / 2)×(N / 2)). These pooled images 1905a-1905n may then be combined into a single output image 1906, with each of the pooled images 1905a-1905n being processed as a separate channel in the output image 1906. This output image 1906 may then be provided as an input to the next stage 1902a-1902n, as shown in FIG.

[0113] Returning to the discussion of Figure 18, after the final output image 1803 has been created through various stages of processing 1802a-1802n, it may be provided as input to a fully connected layer that processes the output image and classifies the input image into one of a plurality of categories. The plurality of categories may include, and may, for example, consist of, various types of images that may be captured (e.g., WBC, RBC), including types of images whose presence may trigger a review instruction (e.g., platelet clumps).

[0114] Exemplarily, the trained CNN may comprise the following layers: i. An input layer that receives a 128x128x3 RGB image representing a red blood cell image, followed immediately by ii. A convolutional layer with 64 5x5 filters and a ReLU activation function, followed immediately by iii. 2x2 max pooling to produce a 64x64x64 output, followed immediately by iv. A convolutional layer with 128 5x5 filters and a ReLU activation function, followed immediately by v. 2x2 max pooling to produce a 32x32x128 output, followed immediately by vi. A convolutional layer with 256 5x5 filters and a ReLU activation function, followed immediately by vii. 2x2 max pooling to produce a 16x16x256 output, followed immediately by viii. A convolutional layer with 12 5x5 filters and a ReLU activation function, followed immediately by ix. 2x2 max pooling to produce an 8x8x512 output, followed immediately by A convolutional layer with 12 x 5x5 filters and a ReLU activation function, followed immediately by xi.2x2 max pooling to produce a 4x4x512 output, followed immediately by xii. A fully connected layer that produces K scalar values, where K is the number of categories into which the cell images are classified. For example, if the NN is trained to classify cell images into one of forward and backward categories, K is equal to 2. For example, if the NN is trained to classify cell images into one of the following categories, K is equal to 5.

[0115] These classifications may then be compared to thresholds (e.g., expected percentages or numbers of particular particle types), and if one or more thresholds are exceeded (or, in the case of lower thresholds, not met), a system implemented according to the present disclosure may determine that corresponding review instructions (e.g., flags) should be presented to the user 603. For example, an abnormal cell type may be flagged as abnormal if it exceeds a certain percentage (illustratively, if RBG fragments exceed a 2.5% threshold)—or alternatively, an abnormal cell type may be flagged as abnormal if it exceeds a certain count in the blood sample (illustratively, more than three blasts). These counts or specific percentages can be based on customized, programmed rules, rules set by the user, or rules derived from practical lab standards. These review instructions may be provided, in the case of abnormal particle types, along with an explanation indicating the abnormal particle type that triggered the instruction. These review instructions are particularly useful for pointing out abnormal particle types to the user and allow the user to review any associated abnormal particle images on-screen to help identify the abnormal particle type without having to perform follow-up confirmatory tests (e.g., smears).

[0116] When a cell is classified, there will likely be some type of score associated with the cell. For example, a cell would have to exceed a certain classification threshold to be labeled as a first cell type (e.g., platelet), then a further classification threshold to be labeled as an abnormal cell type (e.g., giant platelet), and finally a certain numerical threshold would need to be exceeded for a review instruction associated with the listed abnormal cell type (e.g., flag for giant platelets). Illustratively, an imaged cell may need to exceed a 60% confidence score to be assigned as a platelet and a 50% confidence score to be assigned as a giant platelet (or alternatively, once assigned as a platelet, the imaged cell would be sent to a sub-classifier, and that sub-classification would need to exceed a certain threshold—e.g., 70% to be assigned as a giant platelet), and then the total number of giant platelets would need to exceed a numerical threshold (e.g., 2.5%) for the sample to be flagged for giant platelets. Note that these are illustrative examples and any range of confidence scores and numerical thresholds may be used, and perhaps different confidence scores and different numerical thresholds may be used for different cell types.

[0117] Additionally, the review instructions for abnormal cell types may differ from the image review for abnormal cell types. For example, all giant platelets may be observable as images in a separate category specific to their cell type (e.g., a giant platelet cell category associated with images of giant platelets). However, a certain threshold score for that instruction (e.g., 2.5%) would need to be exceeded to trigger the review instruction (the sample would be flagged as having an abnormally large number of giant platelets).

[0118] Examples of such abnormal cell types, along with corresponding descriptions, are provided below in Table 3.

[0119] [Table 2]

[0120] What review instructions may be determined and how they will be determined may be based on characteristics of a particular implementation, such as what data is collected about the sample. To illustrate, consider a system in which both images and impedance are used to identify platelets, where, for convenience, platelet identification is based on images specified by a PLT and platelet identification is based on impedance specified by a PLT-i. In such a case, the platelet results generated using imaging techniques may be the primary parameter for reporting (e.g., displayed on a results screen with other parameters, while the PLT-i results may only be available through a lower-level screen), and both the PLT and PLT-i results may be used to determine whether to provide a notification and accompanying explanation to the user based on logic such as that described below in Table 4.

[0121] [Table 3]

[0122] Another example approach that may be employed, in addition to or as an alternative to the approach described in the context of Table 4, would be to determine a flag based on confidence or test result value. For example, in some cases, an analyzer may be configured with a built-in confidence threshold, and results generated with confidence below this threshold may be accompanied by a confidence flag indicating that the result is of low confidence and may require further review. As another example, in some cases, an analyzer user may be permitted to define various range limits, such as reference limits, action limits, critical limits, etc. In such cases, when a result falls outside of a specified limit range, a flag may be provided indicating the limits that the result falls outside.

[0123] In either case, once the results are determined, an interface may be displayed 604, which may include various parameters and / or review instructions derived from image, impedance, or other data associated with the sample and corresponding descriptions. An example of such an interface is shown in FIG. 7. In the interface shown therein, the user is presented with a worklist 701 containing a set of review instructions 702 and descriptions 703 of those review instructions. The interface of FIG. 7 also provides the user with categorization of the different review instructions (i.e., “Sample Quality” and “Morphology Message”) and concise instructions for the types of review and / or other remedial actions that may be appropriate in light of the displayed review instructions. To assist with this review, the interface of FIG. 7 displays 605 a set of thumbnail cell images 704 corresponding to the images to be reviewed based on the review indicators. For example, if the review indicator description states that platelet clumps were detected in the sample, a set of thumbnail cell images may be presented, displaying thumbnails of images in which platelet clumps were detected. These images may be presented in an order based on their contribution to the corresponding review instructions (e.g., platelet clamp images may be sorted by the size of the clamp shown or the confidence with which the clamp was identified), and when a thumbnail image is clicked or otherwise selected, a full-resolution copy of the image corresponding to the selected thumbnail may be displayed to enable the user to perform the appropriate review task.

[0124] Variations on the above examples are also possible with respect to how the review instructions and thumbnail cellular images may be presented. For example, in some cases, rather than displaying a set of thumbnail cellular images corresponding to an item in the worklist, a user may be provided with a list of parameters and a corresponding review notification, and in response to selecting the notification (or its corresponding parameter), a set of thumbnail cellular images specifically for that parameter may be provided. As another example of possible variations that may exist in some implementations, there are different approaches to presenting thumbnail cellular images. For example, such thumbnail cellular images may be presented in an order sorted according to factors such as capture order, size, shape, standard deviation from the mean, and the like. It is also possible that in some cases, review instructions may be provided that are not associated with a particular image. For example, if a non-imaging modality (e.g., impedance) identifies a particular unexpected cell type in the sample, review instructions may be provided indicating that a reflex test for the unexpected cell type should be performed, but may not be accompanied by (or associated with) a thumbnail cellular image as described above.

[0125] Other types of variations beyond variations in the presentation of review instructions and thumbnail cell images are also possible. To illustrate, consider possible review instructions that may be provided not based on abnormal cell types, but based on results (e.g., counts) obtained for cells that would be expected to be present in a sample (e.g., red blood cells in a whole blood sample). An illustrative example of this type may be a low-confidence flag, which some implementations may provide if the confidence determined for a particular count (e.g., a red blood cell count) is below a built-in threshold of the analyzer that determined the count. In this case, a specific low-confidence review instruction (e.g., a flag that has an overall appearance different from flags that might be displayed for platelet clumps or different types of symbols) may be displayed, and the user may be permitted to view thumbnails of cell images that correspond to the low-confidence review indicator (e.g., images identified as red blood cells with confidence below a threshold). As another example, if the count exceeds a built-in threshold that corresponds to the level that the analyzer claims to be accurate at (e.g., the analyzer claims to be able to accurately count up to X cell type and a count of X+Y of that cell type is detected), a linearity review instruction may be provided along with a thumbnail cell image of the cell type whose count exceeded the threshold and a message indicating that the sample should be diluted and returned.

[0126] As an example of yet another type of variation, in some cases, a user may be able to specify one or more thresholds to be applied to various counts to trigger a review instruction. For example, a user may define a first set of high and low thresholds for a cell type and a second set of high and low thresholds for that cell type. In this case, if the counts for that cell type fall outside the first set of high and low thresholds but not the second set of high and low thresholds, a review instruction having a first characteristic (e.g., a yellow flag) may be provided, while if the counts for that cell type fall outside the second set of high and low thresholds, a review instruction having a second characteristic (e.g., a red flag) may be provided. Thus, the examples of review instructions and their possible triggers provided above should be understood as merely illustrative and should not be treated as limiting on the scope of protection provided by this document or any other document claiming the benefit of this document.

[0127] Multi-Channel Systems As discussed herein, a sample may be partitioned (e.g., divided into aliquots) to enable various types of testing. Thus, in some embodiments, a sample analysis system may include an aliquoter configured to separate the sample into aliquots, and a controller (e.g., a processor) is programmed to cause the fluidics system to control the flow of the aliquots based on parameters requiring determined values.

[0128] Referring now to FIG. 8 , an illustrative flow diagram for a dual-channel system is shown. As described in more detail below, the dual-channel system may be configured to capture high-quality images of microparticles in a first aliquot of a sample fluid (e.g., blood cells) in a flow cell with an imaging system according to the above description, while analyzing a second aliquot of the same sample fluid with a suitable alternative system according to the description herein. In some embodiments, and as shown, the system may capture 801 images of the blood cells (e.g., an aliquot) in the flow cell and measure 802 the impedance of the blood cells passing through the alternative system. Thus, in the present illustrative example, the dual-channel system includes an imaging system according to the above description as well as an impedance system according to the above description. Note that further embodiments may use more than two channels—for example, adding any of a spectrophotometric channel, a fluorescence channel, a conductivity channel, a light scattering channel, or a VCS channel. Although the term channel is used, that term may also be used synonymously with module and is intended to refer to the use of different analytical processes to analyze particles - in this concept, each channel or module uses a different analytical technique for a particular analysis (e.g., an imaging technique which is different from an impedance technique, which is different from a spectrophotometric technique).

[0129] While the illustrative example shown in FIG. 8 describes measuring 802 the impedance of blood cells passing through an alternative system, it should be understood that blood cells in a sample fluid may be analyzed using alternative systems that may not measure impedance, such as the fluorescence image analyzer 2001 and / or spectrophotometer system 3000 described above. While this illustrative example is described with reference to a channel for an imaging system and a channel for an alternative system, it should also be understood that various implementations may include any number of channels using any type of different measurement system (e.g., fluorescence, light scattering, and / or spectrophotometry systems). Thus, it should be understood that a multi-channel system (including, but not limited to, a dual-channel system) may utilize an imaging system having a flow cell 22, a high-light-resolution imaging device 24, and a processor 18 to capture images from a first aliquot of sample fluid, and that other channels of the multi-channel system may include any other suitable systems configured to appropriately analyze other aliquots of sample fluid.

[0130] As images are captured 801 and impedance is measured 802, the system may utilize an analysis module to determine values ​​for a first plurality of parameters using data from the flow imaging module 803 and to determine values ​​for a second plurality of parameters using data from an alternative system (or any other alternative system as would be apparent to one of ordinary skill in the art in view of the teachings herein) 804. By way of example, the system may determine one or more image-based numeric values ​​803 based on analysis of the captured 801 images of blood cells and determine one or more numeric parameters 804 based on measurements 802 from an alternative system (e.g., an impedance system).

[0131] The first and second parameters may then be analyzed to determine a confidence score or review indication 805. The first and second parameters may also be analyzed for any other suitable purpose as would be apparent to one of ordinary skill in the art in view of the teachings herein 805. Alternatively, the system may present the determined values ​​803, 804 (which may include one or more image-based numerical values ​​as well as one or more numerical parameters based on alternative system measurements 802) to the user via a computing interface.

[0132] In some cases, at least one of the first measured parameters from the imaging system described above and at least one of the second parameters measured from a suitable alternative to the multi-channel system (e.g., a two-channel system or two channels in a three or more channel configuration) are similar and / or identical. Similar and / or matching measured parameters from the imaging system and the alternative to the multi-channel system may be utilized by the multi-channel system for any suitable purpose, as would be apparent to one skilled in the art in view of the teachings herein.

[0133] In a further embodiment, the first parameter (e.g., a parameter associated with the captured image) may include, but is not limited to, percent nucleated red blood cells, nucleated red blood cell count, percent undifferentiated white blood cells, undifferentiated white blood cell count, percent neutrophils, neutrophil count, percent immature granulocytes, immature granulocyte count, percent lymphocytes, lymphocyte count, percent monocytes, monocyte count, percent eosinophils, eosinophil count, percent basophils, basophil count, percent reticulocytes, reticulocyte count, and immature reticulocyte fraction. In another embodiment, the second parameter (e.g., a parameter associated with the measured impedance value) may include, but is not limited to, mean cell volume, mean corpuscular hemoglobin, mean corpuscular hemoglobin concentration, red blood cell distribution width, standard deviation of red blood cell distribution width, and mean platelet volume.

[0134] Unclassified cells refer to cells that fail to exceed a specific classification threshold to be assigned to a cell type. In various examples, unclassified cells may be placed in a general unclassified classification bucket, and the image may be presented for user review (e.g., manually labeling / classifying these cells on a screen). Cells labeled as unclassified white blood cells may be classified as white blood cells (e.g., exceed a first confidence threshold for classification as a white blood cell), but may fail to meet the confidence threshold for classification as a particular type of white blood cell (e.g., one in a 5-part or 6-part WBC fraction).

[0135] Processing Architecture Referring now to FIG. 9, that figure is a simplified block diagram of an exemplary module system that may be used to implement various logic and / or control various components described herein. The module system 900 may be part of or connected to a cellular analysis system. The module system 900 is suitable for generating data related to an analysis or receiving input related to an analysis. In some cases, the module system 900 includes hardware elements electrically coupled via a bus subsystem 902, including one or more processors 904, one or more input devices 906, such as user interface input devices, and / or one or more output devices 908, such as user interface output devices. In some cases, the system 900 includes a network interface 910 and / or a diagnostic system interface 940, which can receive signals from and / or send signals to a diagnostic system 942. In some cases, system 900 includes software elements shown here as currently residing in operating memory 912 of memory 914, for example, an operating system 916, and / or other code 918, such as programs configured to implement one or more aspects of the techniques disclosed herein.

[0136] In some embodiments, module system 900 may include a storage subsystem 920 that can store basic programming and data structures that provide the functionality of the various techniques disclosed herein. For example, software modules that implement the functionality of aspects of the methods described herein may be stored in storage subsystem 920. These software modules may be executed by one or more processors 904. In a distributed environment, software modules may be stored on multiple computer systems and executed by processors of multiple computer systems. Storage subsystem 920 may include a memory subsystem 922 and a file storage subsystem 928. Memory subsystem 922 may include multiple memories, including a main random-access memory (RAM) 926 for storing instructions and data during program execution and a read-only memory (ROM) 924 in which fixed instructions are stored. File storage subsystem 928 may provide persistent (non-volatile) storage for program and data files and may include a tangible storage medium that may optionally embody patient, treatment, assessment, or other data. The file storage subsystem 928 may include hard disk drives, floppy disk drives with associated removable media, Compact Digital Read Only Memory (CD-ROM) drives, optical drives, DVDs, CD-Rs, CD-RWs, solid state removable memory, other removable media cartridges or disks, and the like. One or more of the drives may be located at remote locations on other connected computers at other sites coupled to the module system 900.In some cases, the system may include a computer-readable or other tangible storage medium storing one or more sequences of instructions or code that, when executed by one or more processors, cause the one or more processors to perform any aspect of the techniques or methods disclosed herein. One or more modules implementing functionality of the techniques disclosed herein may be stored by file storage subsystem 928. In some embodiments, software or code will provide a protocol for enabling module system 900 to communicate with communications network 930. Optionally, such communications may include dial-up or internet connection communications.

[0137] It will be appreciated that system 900 can be configured to perform or cause a system to perform various aspects of the methods as described herein. For example, processor component 904 can optionally be a microprocessor control module configured to receive cellular parameter signals from sensor input device or module 932, from user interface input device 906, and / or from diagnostic system 942 via diagnostic system interface 940 and / or network interface 910 and communications network 930. Processor component 904 can also optionally be configured to transmit cellular parameter signals, processed according to any of the techniques disclosed herein, to sensor output device or module 936, to user interface output device 908, to network interface device 910, to diagnostic system interface 940, or any combination thereof. Each of the devices or modules described herein can include one or more software modules on a computer-readable medium that are processed by a processor or hardware module, or any combination thereof.

[0138] The user interface input devices 906 may include, for example, pointing devices such as a touchpad, keyboard, mouse, trackball, graphics tablet, scanner, joystick, etc., touch screen integrated into a display, audio input devices such as a voice recognition system, microphone, and other types of input devices. The user interface input devices 906 may download computer-executable code from a tangible storage medium or from a communications network 930, the code embodying any of the methods or aspects thereof disclosed herein. It will be appreciated that terminal software may be updated from time to time and downloaded to the terminal as needed. In general, use of the term "input device" is intended to encompass a variety of conventional and proprietary devices and methods for inputting information into the module system 900.

[0139] The user interface output devices 906 may include, for example, a display subsystem, a printer, a fax machine, or a non-visual display such as an audio output device. The display subsystem may also provide a non-visual display, such as via an audio output device. In general, use of the term “output device” is intended to include a variety of conventional and proprietary devices and methods for outputting information from the module system 900 to a user. The bus subsystem 902 provides a mechanism for allowing the various components and subsystems of the module system 900 to communicate with each other as intended or desired. The various subsystems and components of the module system 900 need not be in the same physical location but may be distributed at various locations within a distributed network. While the bus subsystem 902 is shown schematically as a single bus, alternative embodiments of the bus subsystem may utilize multiple buses.

[0140] The network interface 910 may provide an interface to an external network 930 or other devices. The external communications network 930 may be configured to communicate with other parties as needed or desired. As such, the external communications network 930 may receive electronic packets from the module system 900 and transmit any information back to the module system 900 as needed or desired. As shown here, the communications network 930 and / or the diagnostic system interface 940 may transmit information to or receive information from the diagnostic system 942. In addition to providing such infrastructure communications links internal to the system, the communications network 930 may provide connectivity to other networks, such as the Internet, and may comprise wired, wireless, modem, and / or other types of interfacing connections. It is also possible that the network interface 910 may enable one module system to interface with one or more other systems to collectively provide functionality as described herein. For example, in some cases, a first module system local to the analyzer may control the analyzer, coordinate its various components, and collect data about the sample, while a second module system located remotely (e.g., a cloud system separated from the first module system via a wide area network) may receive the data from the first module system, analyze it, and provide results that may be provided on the user interface output device 908 of the first module system.

[0141] It will be apparent to those skilled in the art that considerable variations may be used depending on specific requirements. For example, customized hardware may also be used and / or particular elements may be implemented in hardware, software (including portable software such as applets), or both. Additionally, connections to other computing devices, such as network input / output devices, may be used. The module system 900 itself can be of various types, including a computer terminal, a personal computer, a portable computer, a workstation, a network computer, or any other data processing system. Due to the ever-changing nature of computers and networks, the description of the module system 900 shown in FIG. 9 is intended solely as a specific example for illustration. Many other configurations of the module system 900 are possible, having more or fewer components than the module system shown in FIG. 9. Any of the modules or components of the module system 900, or any combination of such modules or components, may be combined with, integrated into, or otherwise configured to be in connection with, any of the embodiments of the cellular analysis system disclosed herein. Relatedly, any of the hardware and software components discussed above may be integrated with or configured to interface with other medical evaluation or treatment systems used in other locations.

[0142] Sample preparation process example In the systems described herein, a process such as that shown in FIG. 11 may be used to perform sample preparation before a sample fluid is analyzed according to the description herein. First, in the process of FIG. 8, a staining agent may be delivered to a chamber, such as a mixing chamber, RBC chamber 4015, and / or WBC chambers 4020, 4025, as described herein, in step 2601. This may include, for example, delivering the staining agent to the chamber via a staining agent dispenser. The staining agent may then be preheated in the chamber, such as by induction heating, in step 2602. Next, a sample may be delivered to the chamber in step 2603. This may include, for example, delivering the sample to the chamber through a sample dispenser (e.g., probe 4005) for addition to the staining agent. In some embodiments, delivering the sample to the chamber may include mixing the sample with the preheated staining agent in the chamber. In the process of FIG. 11, a homogenous sample mixture may then be formed in the chamber in step 2604. This may involve using fluid energy to mix the sample and stain, such as by circulating the sample out of the chamber and pushing it back into the chamber through corresponding tangential ports in the housing to perform regurgitative mixing. Alternatively, this may involve using a magnet to drive a spherical ferromagnetic ball disposed within the chamber to perform vortex mixing. As another example, this may involve introducing one or more gas bubbles into the bottom of the chamber to create a vortex.

[0143] The homogenous sample mixture may then be heated in the chamber, such as by inductive or resistive heating, in step 605. In some embodiments, the homogenous sample mixture may be heated to a threshold temperature by inductive or resistive heating and then maintained at the threshold temperature by a maintenance heater.

[0144] After the homogeneous sample mixture reaches a threshold temperature, the sample mixture may be transported to a flow cell, such as flow cell 22 in FIG. 1, to be imaged by a camera, such as high light resolution imaging device 24 in FIG.

[0145] Although the formation and inductive heating of the sample mixture has been described as occurring within a chamber, it will be appreciated that an alternative arrangement may include tubing having a lumen (not shown) within which the sample mixture may be formed and inductively heated in a manner similar to that described above. Additionally or alternatively, any one or more of the teachings herein may be combined with any one or more of the teachings disclosed in U.S. Patent No. 9,429,524, issued August 30, 2016, and entitled "Systems and Methods for Imaging Fluid Samples," the disclosure of which is incorporated by reference in its entirety.

[0146] In some embodiments, the addition of a diluent is part of a preparation step, and the diluent is added to each chamber before, after, or both before and after the blood sample is added to each chamber. For example, the RBC chamber can receive a diluent as the primary or only preparation reagent, while the WBC chamber can receive both a diluent and a stain.

[0147] It should be appreciated that the preparation steps for the RBC chamber can differ from those for the WBC chamber. For example, the RBC chamber will utilize preparation steps that involve receiving a) a diluent followed by a blood sample, b) a blood sample followed by a diluent, or c) a diluent followed by a blood sample followed by further diluent, but will not receive a stain. Thus, the preparation time for the RBC chamber may be shorter, and the workflow can involve running the RBC sample through the imaging process while the WBC sample is still being prepared.

[0148] In some embodiments, the staining reagent utilizes both a lysing agent (to lyse red blood cells) and a staining agent (to penetrate remaining white blood cells, stain the interior area, and repair the white blood cells so the staining agent does not flow out). Thus, a single staining reagent can be used to process a specific type of cell (e.g., white blood cells) to both eliminate red blood cells and stain remaining white blood cells. Other embodiments can utilize multiple compositions, e.g., a first lysing reagent to lyse red blood cells and a second staining reagent to stain white blood cells, and the workflow would involve a chamber (e.g., a WBC chamber) receiving a separate lysing reagent and a separate staining reagent to prepare the WBC sample for visualization.

[0149] In some embodiments, the various chambers (e.g., the RBC chamber 4015 and the WBC chambers 4020, 4025) are not intended to strictly prepare dedicated cell types, or in other words, cell types can be rotated. For example, chambers can be used alternately for RBC and WBC preparation. Thus, once the sample in a chamber has been prepped and imaged, a wash cycle can be utilized to wash the chamber before receiving a subsequent blood sample (e.g., a chamber can be configured to first prepare WBCs for a specific volume of the same preparation run, then RBCs for a specific volume of sample preparation run, e.g., one WBC preparation followed by one RBC preparation, or two WBC preparations followed by one RBC preparation followed by two more WBC preparations, etc.). Washing reagents, such as diluents or detergents, can be used between sample runs to eliminate carryover. Even in situations where a particular chamber is only used for a particular cell type (e.g., the 4020 is used solely as a WBC chamber), there can be a wash step run after the sample is prepared and imaged to eliminate carryover.

[0150] Other embodiments may also utilize multiple stains as part of the preparation process. For example, a first stain is configured to stain white blood cells in the manner described herein, and a second stain is configured to stain at least one of platelets or reticulocytes. These staining compositions may be used independently in various workflows. For example, a first chamber may be used to prepare a white blood cell sample, which preparation includes receiving at least a WBC stain and a lytic reagent, while a second chamber may be used to prepare a platelet sample—this chamber will receive at least a platelet reagent—different from the WBC stain and lytic reagent.

[0151] While the terms white blood cell (WBC) chamber and red blood cell (RBC) chamber are used to refer to sample preparation chambers for imaging, it should be noted that the sample imaged as a result of the preparation process can enable biological imaging of multiple cell types. For example, a WBC chamber uses a lysing agent to eliminate red blood cells; however, the lysing agent may still retain platelets and reticulocytes, so a sample prepared in the WBC chamber can still image, for example, at least white blood cells, platelets, and reticulocytes. Similarly, an RBC chamber may undergo a different preparation procedure than a WBC chamber (e.g., no lysing agent or no stain / lysing agent combination reagent), but a sample prepared in the RBC chamber can still visualize multiple cell types, such as red blood cells and one or more of white blood cells, platelets, and reticulocytes. Further information regarding how samples may be prepared for analysis in some embodiments, and particularly how stains may be applied in some cases, is provided in U.S. Patent Application No. 18 / 224,947, the disclosure of which is incorporated herein by reference in its entirety.

[0152] Further Examples To further illustrate possible implementations and embodiments of the disclosed technology, exemplary systems and methods that may be implemented in accordance with the present disclosure are described below.

[0153] Example 1A A sample analysis system comprising: a) a flow cell; b) a fluidics system adapted to flow a portion of a sample through the flow cell; c) an image capture device configured to capture multiple images of blood cells as the blood cells pass through the flow cell; and d) one or more processors, the one or more processors programmed to perform actions including: i) analyzing the multiple images to determine whether review instructions apply to the multiple images; ii) displaying an interface having the review instructions and a description of the review instructions; and iii) displaying an interface having at least one cell image corresponding to the review instructions.

[0154] Example 2A The sample analysis system of Example 1A, wherein the one or more processors are configured to determine that user-specified review conditions are satisfied and, in response to determining that the user-specified review conditions are satisfied, display an interface having review instructions.

[0155] Example 3A The sample analysis system of Example 1A, wherein the review indication is at least one of a high count indication or a low count indication.

[0156] Example 4A The sample analysis system of Example 1A, wherein the one or more processors are configured to determine whether review instructions should be displayed based on satisfaction of built-in review conditions.

[0157] Example 5A The sample analysis system of Example 1A, wherein the one or more processors are configured to determine whether review instructions should be displayed based on at least one of the low-confidence condition being satisfied and the linearity condition not being satisfied.

[0158] Example 6A The sample analysis system of Example 1A, wherein the one or more processors are programmed to determine whether review instructions should be displayed based on detecting at least one of platelet clumps or red blood cell clumps within the multiple images of blood cells.

[0159] Example 7A The sample analysis system of Example 1A, wherein the one or more processors are programmed to determine whether review instructions should be displayed based on detecting at least one of a red blood cell fragment, a sickle cell, a dimorphic red blood cell, a large platelet, a giant platelet, a reticulocyte, a variant lymphocyte, or a blast cell within the multiple images of blood cells.

[0160] Example 8A The sample analysis system of Example 1A, wherein the interface includes a plurality of review instructions, and the interface displays a description of each review instruction and at least one cell image corresponding to each review instruction.

[0161] Example 9A The sample analysis system of Example 1A further comprises a non-transitory computer-readable medium, the non-transitory computer-readable medium storing a machine learning algorithm on the non-transitory computer-readable medium that is trained to analyze images from the plurality of images and analyze particles that appear in the images, and the one or more processors are programmed to determine that review instructions are to be applied to the plurality of images based on confidence scores provided by the machine learning algorithm for classification of particles that appear in the plurality of images.

[0162] Example 10A The sample analysis system of Example 1A further comprises a non-transitory computer-readable medium that stores a plurality of conditions for determining whether corresponding review instructions should be provided, the plurality of conditions including a set of user-defined conditions that can be modified by a user of the sample analysis system and a set of built-in conditions that cannot be modified by a user of the sample analysis system.

[0163] Example 11A The sample analysis system of Example 10A, wherein a) each user defined from a set of user-defined conditions is associated with a specific cell type; b) the set of user-defined conditions includes a first set of high and low thresholds for the specific cell type and a second set of high and low thresholds for the specific cell type; c) the one or more processors are programmed to: i) determine that a first review instruction is applied to the plurality of images when the count for the specific cell type falls outside the first set of high and low thresholds and within the second set of high and low thresholds; ii) determine that a second review instruction is applied to the plurality of images when the count for the specific cell type falls outside the second set of high and low thresholds; and d) the first review instruction and the second review instruction are visually distinguishable from each other.

[0164] Example 12A The sample analysis system of Example 11A, wherein the first review instruction and the second review instruction have different colors.

[0165] Example 13A The sample analysis system of Example 1A, wherein at least one cell image corresponding to the review instruction includes a thumbnail cell image, and the one or more processors are programmed to display a full-resolution image of the blood cell captured by the image capture device corresponding to the thumbnail cell image in response to receiving a signal indicating user selection of the thumbnail cell image.

[0166] Example 14A The sample analysis system of Example 1A, wherein the at least one cell image corresponding to the review instruction includes a plurality of thumbnail cell images corresponding to the review instruction, and the plurality of thumbnail cell images corresponding to the review instruction are sorted based on their respective contributions to the review instruction.

[0167] Example 15A A sample analysis system of Example 1A, wherein: a) one or more processors include: i) a first processor programmed to analyze a plurality of images to determine whether review instructions apply to the plurality of images, and ii) a second processor programmed to display an interface; b) the second processor is configured by an analyzer that also includes a flow cell and a fluidics system; and c) the first processor is not configured by the analyzer, is separated from the second processor by a wide area network, and communicates with the second processor via the wide area network.

[0168] Example 16A A method for analyzing a sample, comprising: a) using a fluidics system to flow a portion of the sample through a flow cell; b) using an image capture device to capture multiple images of blood cells as they pass through the flow cell; and c) using one or more processors, the one or more processors performing a set of actions including: i) analyzing the multiple images to determine whether review instructions apply to the multiple images; ii) displaying an interface having the review instructions and a description of the review instructions; and iii) displaying an interface having at least one cell image corresponding to the review instructions.

[0169] Example 17A The sample analysis method of Example 16A, including determining that user-specified review conditions are satisfied, and displaying the interface is performed in response to determining that the user-specified review conditions are satisfied.

[0170] Example 18A The method of analyzing a sample of Example 16A, wherein the review indication is at least one of a high count indication or a low count indication.

[0171] Example 19A The sample analysis method of Example 16A, wherein analyzing the plurality of images to determine whether review instructions apply to the plurality of images includes determining that the review instructions should be displayed based on satisfaction of built-in review conditions.

[0172] Example 20A The sample analysis method of Example 16A, wherein analyzing the plurality of images to determine whether review instructions apply to the plurality of images includes determining whether the review instructions should be displayed based on at least one of a low confidence condition being satisfied and a linear condition not being satisfied.

[0173] Example 21A The sample analysis method of Example 16A, wherein analyzing the plurality of images to determine whether review instructions apply to the plurality of images includes determining whether the review instructions should be displayed based on detecting at least one of platelet clumps or red blood cell clumps within the plurality of images of blood cells.

[0174] Example 22A The sample analysis method of Example 16A, wherein analyzing the plurality of images to determine whether review instructions apply to the plurality of images includes determining whether the review instructions should be displayed based on detecting at least one of red blood cell fragments, sickle red blood cells, biphasic red blood cells, large platelets, giant platelets, reticulocytes, atypical lymphocytes, or blast cells within the plurality of images of blood cells.

[0175] Example 23A The sample analysis method of Example 16A, wherein the interface includes a plurality of review instructions, and the interface displays a description of each review instruction and at least one cell image corresponding to each review instruction.

[0176] Example 24A The sample analysis method of Example 16A, wherein analyzing the plurality of images to determine whether review instructions apply to the plurality of images includes: a) using a machine learning algorithm trained to analyze images from the plurality of images and analyze particles shown in the images, and b) determining that the review instructions apply to the plurality of images based on confidence scores provided by the machine learning algorithm for classification of particles shown in the plurality of images.

[0177] Example 25A The sample analysis method of Example 16A, wherein analyzing the plurality of images to determine whether review instructions apply to the plurality of images includes retrieving from a non-transitory computer-readable medium a plurality of conditions for determining whether corresponding review instructions should be provided, the plurality of conditions including a set of user-defined conditions that can be modified by a user of the sample analysis system and a set of built-in conditions that cannot be modified by a user of the sample analysis system.

[0178] Example 26A The sample analysis method of Example 25A, wherein a) each user defined from a set of user-defined conditions is associated with a specific cell type; b) the set of user-defined conditions includes a first set of high and low thresholds for the specific cell type and a second set of high and low thresholds for the specific cell type; c) the method includes: i) determining whether first review instructions apply to the plurality of images based on whether the count for the specific cell type falls outside the first set of high and low thresholds and within the second set of high and low thresholds; ii) determining whether second review instructions apply to the plurality of images based on whether the count for the specific cell type falls outside the second set of high and low thresholds; and d) the first review instructions and the second review instructions are visually distinguishable from each other.

[0179] Example 27A The sample analysis method of Example 26A, wherein the first and second review instructions have different colors.

[0180] Example 28A The sample analysis method of Example 16A, wherein a) at least one cell image corresponding to the review instruction includes a thumbnail cell image, and b) the method includes: i) receiving a signal indicating a user selection of the thumbnail cell image; and ii) in response to receiving the signal indicating the user selection of the thumbnail cell image, displaying a full resolution image of a blood cell captured by the image capture device corresponding to the thumbnail cell image.

[0181] Example 29A The sample analysis method of Example 16A, wherein the at least one cell image corresponding to the review instruction includes a plurality of thumbnail cell images corresponding to the review instruction, and the method includes sorting the plurality of thumbnail cell images corresponding to the review instruction based on their respective contributions to the review instruction.

[0182] Example 30A The sample analysis method of Example 16A, wherein: a) the one or more processors include: i) a first processor programmed to analyze the plurality of images to determine whether review instructions apply to the plurality of images, and ii) a second processor programmed to display an interface; b) the second processor is configured with an analyzer that also includes a flow cell and a fluidics system; and c) the first processor is not configured with the analyzer, is separated from the second processor by a wide area network, and communicates with the second processor via the wide area network.

[0183] Example 31A A method of using a biological analyzer, comprising: a) using a fluidics system to flow a portion of a sample through a flow cell; b) using an image capture device to capture multiple images of blood cells as they pass through the flow cell; c) observing review instructions associated with the sample; and d) reviewing the review instructions by accessing data corresponding to the review instructions through a user interface.

[0184] Example 32A The method of Example 31A, wherein a) the review instructions associated with the sample are associated with at least a portion of the plurality of images, and b) reviewing the review instructions by accessing data corresponding to the review instructions through a user interface is performed by reviewing at least a subset of the at least portion.

[0185] Example 33A The method of Example 32A, wherein a) the method includes: i) viewing a set of thumbnails of cell images having a type corresponding to the review instructions; and ii) selecting one thumbnail from the set of thumbnails; and b) reviewing at least a portion of the subset of the plurality of images includes viewing a full resolution image corresponding to the selected thumbnail.

[0186] Example 34A The method of Example 33A, comprising selecting a sorting criterion for the set of thumbnails of cell images having a type corresponding to the review instruction.

[0187] Example 35A The method of Example 32A, wherein a) accessing data corresponding to the review instructions includes reviewing a message indicating an abnormal measurement value derived from multiple images of blood cells, and b) the method includes confirming whether the abnormal measurement value derived from the multiple images is corrected based on reviewing further information corresponding to the abnormal result.

[0188] Example 36A The method of Example 35A, wherein determining whether the abnormal measurement derived from the plurality of images is corrected based on reviewing further information corresponding to the abnormal result includes observing one or more full resolution images from the plurality of images of the blood cells.

[0189] Example 37A The method of Example 35A, wherein determining whether the abnormal measurement values ​​derived from the plurality of images are corrected based on reviewing further information corresponding to the abnormal results includes observing results derived by a non-imaging measurement system.

[0190] Example 38A The method of Example 37A, wherein the anomaly measurement derived from the multiple images is a count for a type of cell and the result derived by the non-imaging measurement system is a count for the same type of cell.

[0191] Example 39A The method of Example 38A, comprising determining whether to perform counting for the same type of cell using a new portion of the sample based on determining whether the aberrant measurements derived from the multiple images are corrected.

[0192] Example 40A The method of Example 31A, further comprising, for at least one cell type from the plurality of cell types, defining review conditions for the cell type.

[0193] Example 41A The method of Example 40A, wherein the review conditions include a plurality of sets of thresholds, each set of thresholds including a high threshold and a low threshold.

[0194] Example 42A The method of Example 31A, comprising determining that further analysis should be performed on the sample based on accessing data corresponding to the review instructions through a user interface.

[0195] Example 43A The method of Example 42A, wherein a) the further analysis includes capturing an image of reticulocytes in the sample, b) the method includes a user accessing one or more images of the reticulocytes, and c) accessing data corresponding to the review instructions through the user interface includes accessing a reticulocyte count for the sample.

[0196] Example 44A The method of Example 42A, wherein a) accessing data corresponding to the review instructions includes reviewing a message indicating that a count for the sample based on a portion of the sample exceeds the maximum allowable count, and b) further analysis includes redetermining the count using a new portion of the sample.

[0197] Example 45A The method of Example 44A, comprising diluting a new portion of the sample to a dilution level higher than the dilution level used on the portion of the sample that formed the basis for the count that exceeded the maximum allowable count.

[0198] Example 1B 1. A sample analysis system comprising: a) a fluidics system adapted to: i) flow a first portion of a blood sample through a first module, the first module being a flow imaging module comprising a flow cell and an image capture device configured to capture a plurality of images of cells in the first portion of the blood sample; and ii) flow a second portion of the blood sample through a second module, the second module being configured to test one or more numerical parameters of the cells in the second portion of the blood sample; and b) one or more processors, the one or more processors programmed to: i) determine the one or more numerical parameters of the cells in the second portion of the blood sample; and ii) present a computing interface including a plurality of images of the cells in the first portion of the blood sample and the one or more numerical parameters of the cells in the second portion of the blood sample.

[0199] Example 2B The sample analysis system of Example 1B, further comprising an aliquotter configured to separate the blood sample into a plurality of aliquots, the first portion being a first aliquot from the plurality of aliquots and the second portion being a second aliquot from the plurality of aliquots.

[0200] Example 3B The sample analysis system of Example 1B is adapted to: a) receive a blood sample in a container carrying a barcode; b) read the barcode; and c) determine one or more tests for the blood sample based on the barcode.

[0201] Example 4B a) the fluidics system is adapted to flow a first sub-portion of a first portion of the blood sample through a flow imaging module for red blood cell (RBC) imaging in a first flow cell; b) The sample analysis system of Example 1B, wherein the fluidics system is adapted to flow a second subportion of the first portion of the blood sample through the flow imaging module for white blood cell (WBC) imaging, and the second subportion is treated with a staining composition.

[0202] Example 5B The sample analysis system of Example 1B, wherein the second module comprises an impedance analyzer.

[0203] Example 6B The sample analysis system of Example 1B, wherein the second module comprises a fluorescence analyzer.

[0204] Example 7B The sample analysis system of Example 1B, wherein the numerical parameter is selected from mean corpuscular volume, cell count, and hemoglobin concentration.

[0205] Example 8B The sample analysis system of Example 1B, wherein the plurality of images includes an image of a first cell type and an image of a second cell type.

[0206] Example 9B The sample analysis system of Example 1B, wherein the plurality of cells comprises a first cell type, and the computing interface is configured to allow a user to select the first cell type and, in response, display an image of the first cell type.

[0207] Example 10B The sample analysis system of Example 1B, wherein the plurality of cells includes a first cell type and a second cell type, and the computing interface is configured to allow a user to select the first cell type and the second cell type and, in response, display images of the first cell type and the second cell type.

[0208] Example 11B The sample analysis system of Example 1B, wherein the one or more processors are further programmed to derive numerical data from the plurality of images and present the numerical data on the computing interface.

[0209] Example 12B The sample analysis system of Example 1B, wherein the second module is further configured to test one or more numerical parameters of the first cell type and test one or more numerical parameters of the second cell type.

[0210] Example 13B The sample analysis system of Example 1B, wherein the second module is configured to determine two or more parameters for the first cell type.

[0211] Example 14B The sample analysis system of Example 1B, wherein the computing interface is configured to provide multiple images of cells of the first portion of the blood sample and one or more numerical parameters of the second portion of the blood sample on a single screen.

[0212] Example 15B A sample analysis system of Example 1B, wherein a) one or more processors include: i) a first processor programmed to determine one or more numerical parameters of cells in the second portion of the blood sample, and ii) a second processor programmed to present a computing interface including a plurality of images of cells in the first portion of the blood sample and one or more numerical parameters of the cells in the second portion of the blood sample; b) the second processor is configured by an analyzer that also includes a fluidics system; and c) the first processor is not configured by the analyzer, is separated from the second processor by a wide area network, and is in communication with the second processor via the wide area network.

[0213] Example 16B 1. A method of sample analysis comprising: a) using a fluidics system that: i) flows a first portion of a blood sample through a first module, the first module being a flow imaging module comprising a flow cell and an image capture device configured to capture a plurality of images of cells in the first portion of the blood sample; and ii) flows a second portion of the blood sample through a second module, the second module being configured to test one or more numerical parameters of the cells in the second portion of the blood sample; and b) using one or more processors that: i) determine the one or more numerical parameters of the cells in the second portion of the blood sample; and ii) presents a computing interface that includes the plurality of images of the cells in the first portion of the blood sample and the one or more numerical parameters of the cells in the second portion of the blood sample.

[0214] Example 17B The sample analysis method of Example 16B, comprising separating the blood sample into a plurality of aliquots using an aliquoter, wherein the first portion is a first aliquot from the plurality of aliquots and the second portion is a second aliquot from the plurality of aliquots.

[0215] Example 18B The sample analysis method of Example 16B, comprising: a) receiving a blood sample in a container carrying a barcode; b) reading the barcode; and c) determining one or more tests for the blood sample based on the barcode.

[0216] Example 19B The sample analysis method of Example 16B, wherein a) the fluidics system is adapted to flow a first sub-portion of the first portion of the blood sample through the flow imaging module for red blood cell (RBC) imaging in a first flow cell, and b) the fluidics system is adapted to flow a second sub-portion of the first portion of the blood sample through the flow imaging module for white blood cell (WBC) imaging, wherein the second sub-portion is treated with a staining composition.

[0217] Example 20B The sample analysis method of Example 16B, wherein the second module comprises an impedance analyzer.

[0218] [Example 21B] The method of analyzing a sample of Example 16B, wherein the second module comprises a fluorescence analyzer.

[0219] Example 22B The sample analysis method of Example 16B, wherein the numerical parameter is selected from mean corpuscular volume, cell count, and hemoglobin concentration.

[0220] Example 23B The method of analyzing a sample of Example 16B, wherein the plurality of images includes an image of a first cell type and an image of a second cell type.

[0221] Example 24B The sample analysis method of Example 16B, wherein the plurality of cells comprises a first cell type, and the computing interface is configured to allow a user to select the first cell type and, in response, display an image of the first cell type.

[0222] Example 25B The sample analysis method of Example 16B, wherein the plurality of cells comprises a first cell type and a second cell type, and the computing interface is configured to allow a user to select the first cell type and the second cell type and, in response, display images of the first cell type and the second cell type.

[0223] Example 26B The sample analysis method of Example 16B, wherein the one or more processors are further programmed to derive numerical data from the plurality of images and present the numerical data on the computing interface.

[0224] Example 27B The sample analysis method of Example 16B, wherein the second module is further configured to test one or more numerical parameters of the first cell type and test one or more numerical parameters of the second cell type.

[0225] [Example 28B] The sample analysis method of Example 16B, wherein the second module is configured to determine two or more parameters for the first cell type.

[0226] Example 29B The sample analysis method of Example 16B, wherein the computing interface is configured to provide multiple images of cells of the first portion of the blood sample and one or more numerical parameters of the second portion of the blood sample on a single screen.

[0227] Example 30B A sample analysis method of Example 16B, wherein a) the one or more processors include: i) a first processor programmed to determine one or more numerical parameters of cells in the second portion of the blood sample; and ii) a second processor programmed to present a computing interface including a plurality of images of cells in the first portion of the blood sample and one or more numerical parameters of the cells in the second portion of the blood sample; b) the second processor is configured by an analyzer that also includes a fluidics system; and c) the first processor is not configured by the analyzer, is separated from the second processor by a wide area network, and is in communication with the second processor via the wide area network.

[0228] Example 1C 1. A sample analysis system comprising: a) a fluidics system adapted to: i) flow a first portion of a blood sample through a first module, the first module being a flow imaging module comprising a flow cell and an image capture device configured to capture a plurality of images of a first type of cell; ii) flow a second portion of the blood sample through a second module, the second module being configured to test one or more numerical parameters of the first type of cell; and b) one or more processors, the one or more processors programmed to: i) determine one or more image-based numerical values ​​of the first cell type from the plurality of images from the first module; ii) determine one or more numerical parameters of the first cell type from the second module; and iii) present a computing interface including the one or more image-based numerical values ​​of the first cell type and the one or more numerical parameters of the first cell type.

[0229] Example 2C The sample analysis system of Example 1C, wherein the first cell type is a red blood cell or a platelet.

[0230] Example 3C The sample analysis system of Example 1C, wherein the fluidics system is adapted to capture a plurality of images of cells of a second type and test one or more numerical parameters of the cells of the second type, and the one or more processors are programmed to determine one or more image-based numerical values ​​of the second cell type from the plurality of images from the first module, determine one or more numerical parameters of the second cell type from the second module, and present a computing interface including the one or more image-based numerical values ​​of the first cell type and the one or more numerical parameters of the second cell type.

[0231] Example 4C The sample analysis system of Example 3C, wherein the first cell type is a red blood cell and the second cell type is a platelet.

[0232] Example 5C The sample analysis system of Example 1C, wherein the first cell type is a red blood cell, the one or more image-based numerical parameters include a red blood cell count, and the one or more numerical parameters include a mean corpuscular volume.

[0233] Example 6C The sample analysis system of Example 1C, wherein the second module comprises an impedance analyzer.

[0234] Example 7C The sample analysis system of Example 1C, wherein the second module comprises a fluorescence analyzer.

[0235] [Example 8C] The sample analysis system of Example 1C, wherein the second module comprises a spectrophotometric analyzer.

[0236] Example 9C The sample analysis system of Example 1C, wherein the first cell type is a platelet, the one or more image-based parameters include a platelet count, and the one or more numerical parameters include a platelet volume.

[0237] Example 10C The sample analysis system of Example 1C, wherein the sample analysis system includes an identification reader configured to read a sample identifier and a controller programmed to determine a parameter for determining a value based on data from the identification reader.

[0238] Example 11C The sample analysis system of Example 10C, wherein the sample analysis system includes an aliquotter configured to separate the sample into aliquots, and the controller is programmed to cause the fluidics system to control the flow of the aliquots based on a parameter for determining a value.

[0239] Example 12C The sample analysis system of Example 1C, wherein the one or more processors are programmed to present on a single screen a computing interface including one or more image-based numerical values ​​of the first cell type and one or more numerical parameters of the first cell type.

[0240] Example 13C The sample analysis system of Example 1C, wherein the first cell type is a red blood cell, the one or more image-based parameters include a red blood cell count, and the one or more numerical parameters include a hemoglobin measurement.

[0241] Example 14C The sample analysis system of Example 1C, wherein the computing interface is configured to allow a user to select a first cell type and then, in response, display a plurality of images of the first cell type.

[0242] Example 15C The sample analysis system of Example 1C, wherein: a) the one or more processors include: i) a first processor programmed to determine one or more image-based numerical values ​​of a first cell type from a plurality of images from the first module; and ii) a second processor programmed to present a computing interface including one or more image-based numerical values ​​of the first cell type and one or more numerical parameters of the first cell type; b) the second processor is configured by an analyzer that also includes a fluidics system; and c) the first processor is not configured by the analyzer, is separated from the second processor by a wide area network, and communicates with the second processor via the wide area network.

[0243] Example 16C 1. A method of analyzing a sample, comprising: a) using a fluidics system that: i) flows a first portion of a blood sample through a first module, the first module being a flow imaging module comprising a flow cell and an image capture device configured to capture a plurality of images of a first type of cell; ii) flows a second portion of the blood sample through a second module, the second module being configured to test one or more numerical parameters of the first type of cell; and b) using one or more processors that: i) determines one or more image-based numerical values ​​of the first cell type from the plurality of images from the first module; ii) determines one or more numerical parameters of the first cell type from the second module; and iii) presents a computing interface that includes the one or more image-based numerical values ​​of the first cell type and the one or more numerical parameters of the first cell type.

[0244] Example 17C The method of analyzing a sample of Example 16C, wherein the first cell type is a red blood cell or a platelet.

[0245] Example 18C The sample analysis system of Example 16C, wherein the fluidics system is adapted to capture a plurality of images of cells of a second type and test one or more numerical parameters of the cells of the second type, and the one or more processors are programmed to determine one or more image-based numerical values ​​of the second cell type from the plurality of images from the first module, determine one or more numerical parameters of the second cell type from the second module, and present a computing interface including the one or more image-based numerical values ​​of the first cell type and the one or more numerical parameters of the second cell type.

[0246] Example 19C The sample analysis method of Example 18C, wherein the first cell type is a red blood cell and the second cell type is a platelet.

[0247] Example 20C The sample analysis method of Example 16C, wherein the first cell type is a red blood cell, the one or more image-based parameters include a red blood cell count, and the one or more numerical parameters include a mean corpuscular volume.

[0248] [Example 21C] The sample analysis method of Example 16C, wherein the second module comprises an impedance analyzer.

[0249] [Example 22C] The method of analyzing a sample of Example 16C, wherein the second module comprises a fluorescence analyzer.

[0250] Example 23C The method of analyzing a sample of Example 16C, wherein the second module comprises a spectrophotometric analyzer.

[0251] [Example 24C] The sample analysis method of Example 16C, wherein the first cell type is platelets, the one or more image-based parameters include platelet count, and the one or more numerical parameters include platelet volume.

[0252] Example 25C The sample analysis method of Example 16C, wherein the sample analysis system includes an identification reader configured to read the sample identifier and a controller programmed to determine parameters for determining values ​​based on data from the identification reader.

[0253] [Example 26C] The sample analysis method of Example 25C, wherein the sample analysis system includes an aliquotter configured to separate the sample into aliquots, and the controller is programmed to cause the fluidics system to control the flow of the aliquots based on a parameter for determining a value.

[0254] [Example 27C] The sample analysis method of Example 16C, wherein the one or more processors are programmed to present on a single screen a computing interface including one or more image-based numerical values ​​of the first cell type and one or more numerical parameters of the first cell type.

[0255] [Example 28C] The sample analyzing method of Example 16C, wherein the first cell type is a red blood cell, the one or more image-based parameters include a red blood cell count, and the one or more numerical parameters include a hemoglobin measurement.

[0256] [Example 29C] The sample analysis method of Example 16C, wherein the computing interface is configured to allow a user to select a first cell type and then, in response, display a plurality of images of the first cell type.

[0257] Example 30C The sample analysis method of Example 16C, wherein: a) the one or more processors include: i) a first processor programmed to determine one or more image-based numerical values ​​of a first cell type from a plurality of images from the first module; and ii) a second processor programmed to present a computing interface including one or more image-based numerical values ​​of the first cell type and one or more numerical parameters of the first cell type; b) the second processor is configured by an analyzer that also includes a fluidics system; and c) the first processor is not configured by the analyzer, is separated from the second processor by a wide area network, and communicates with the second processor via the wide area network.

[0258] interpretation It should be understood that in the above examples and claims, a statement that something is "based on" something else should be understood to mean that it is determined, at least in part, by what is shown as being based on it. To indicate that something must be completely determined based on something else, it is described as being "based EXCLUSIVELY on" whatever it is that must completely determine it.

[0259] It should be understood that a statement that "one or more" or "at least one" of a type of item has a certain characteristic indicates that the items in the indicated group as a whole have that characteristic. To indicate that each item in the group has a certain characteristic, the phrase "each of" would be used in conjunction with a group identifier (e.g., "one or more" or "at least one").

[0260] It is to be understood that within the claims, "set" should be understood as referring to one or more of similar nature, design, or function.

[0261] It should be understood that any of the embodiments described herein may include various other features in addition to or in place of the features described above. By way of example only, any of the embodiments described herein may include one or more of the various features disclosed in any of the various references incorporated herein by reference.

[0262] It should be understood that any one or more of the teachings, expressions, embodiments, examples, etc. described herein may be combined with any one or more of the other teachings, expressions, embodiments, examples, etc. described herein. Thus, the teachings, expressions, embodiments, examples, etc. described above should not be considered in isolation from one another. Various suitable ways in which the teachings herein may be combined will be readily apparent to those skilled in the art in view of the teachings herein. Such modifications and variations are intended to be within the scope of the claims.

[0263] It should be recognized that any patent, publication, or other disclosure material that is said to be incorporated by reference herein, in whole or in part, is incorporated herein only to the extent that the incorporated material does not contradict existing provisions, statements, or other disclosure material set forth in this disclosure. Accordingly, and to the extent necessary, the present disclosure as expressly set forth herein supersedes any conflicting material incorporated herein by reference. Any material, or portions thereof, that is said to be incorporated herein by reference but that contradicts existing provisions, statements, or other disclosure material set forth in this disclosure will only be incorporated to the extent that no contradiction arises between the incorporated material and the existing disclosure material.

[0264] While various versions of the present invention have been shown and described, further adaptations of the methods and systems described herein may be achieved by appropriate modifications by those skilled in the art without departing from the scope of the present invention. Some of such possible modifications have been mentioned, and others will become apparent to those skilled in the art. For example, the embodiments, versions, geometries, materials, dimensions, ratios, steps, and the like discussed above are illustrative and not required. Accordingly, it will be understood that the scope of the present invention should be considered in terms of the appended claims, and not limited to the details of structure and operation shown and described in the specification and drawings. [Explanation of symbols]

[0265] 18 processors 21 Narrowing Zone 21a Proximal flow channel section 21b Distal flow channel portion 22 flow cell 23 Observation Zone 25 Sample fluid source 29 Sample tubes 28 Distal end of sample tube 24 Digital high-resolution imaging device 32 ribbon sample streams 42 Light source 43 Lighting opening 44 Autofocus Target 46 Objective lenses, optical elements 52 Displacement distance 54 Motor Drive 55 Holder 55a, 55b Angle adjustment pivot point 100c blood analyzer 63 Display 200 Cell Analysis System 210 Preparation System 220 Transducer Module 230 Analysis System 250 Reports 260 Waste Systems 300 Cell Analysis System 330 flow cell 304 Analysis System 306 Report 308 Waste Systems 310 Transducer Module 332 Cell Interrogation Zone 334, 336 electrode 400 Analysis system or transducer, system 410 Optical Elements 412 Cell Interrogation Zone 420 Flow path 422 Sample Stream 424 Sheath Fluid 430 Electrode Assembly 432 First electrode mechanism 434 Second electrode mechanism 900 Module System 902 Bus Subsystem 904 Processors, Processor Components 906 User Interface Input Devices 908 User Interface Output Device 910 Network Interface 912 Operating Memory 914 memory 916 Operating Systems 918 Other Codes 920 Memory Subsystem 928 File Storage Subsystem 930 Communication Network, External Communication Network 932 Sensor Input Device or Module 936 Sensor Output Device or Module 940 Diagnostic System Interface 942 Diagnostic System 2000 Flow Cytometer 2006 Control Unit 2020 Nozzle 2021 light source 2022 Collimating Lens 2023 Sheath Flow Cell 2024 Condenser Lens 2025 pinhole plate 2026 Forward scattered light detector 2027 Condenser Lens 2028 Dichroic Mirror 2028' filter 2029 Side scattered light detector 2030 Pinhole Plate 2031 Side Fluorescence Photodetector 2032, 2033, 2034 Amplifiers 2900 Cell Analysis System 2902 Preparation System 2910 Transducer Module 2912 Laser 2914 Beam 2922 incident beam 2920 Focus Alignment System 2930 Flow Cell 2932 Cell Interrogation Zone 2934 First Electrode 2936 Second Electrode 2940 Light Propagation 2950 Photodetector Assembly 2950A Light Scattering Detector Unit 2950B Light Scattering and Transmission Detector Unit 2951 Opening 3000 spectrophotometer 3017 Temperature Sensor 3021a light source 3021b lens 3021c Prism 3021d cuvette 3021e detector 3024 processor 3025 memory 4000 Test Equipment 4005 Probe, Sample Probe 4010 Suction Pump 4100 Non-imaging system 4200 Imaging System 2212 First Bus (WBC Bus) 2214 Second Bus (RBC Bus) 2221 Sample Analyzer 2222 Air Pressure Transducer 2246 Waste Receptacle 2230 Diluent Reservoir 2232 Dissolving Agent Reservoir 2233 Detergent Reservoir 2241 Sweep Tank 4215 RBC Chamber 4200 Imaging System 4220 1st WBC chamber, WBC chamber 4225 Second WBC chamber, WBC chamber 4233 Flow Cell 4230 Imaging Component 4235 Dyeing agent 4240 Diluent 4245 Sheath 4250 Waste Container

Claims

1. 1. A sample analysis system comprising: a) a flow cell; b) a fluidics system adapted to flow a portion of the sample through said flow cell; c) an image capture device configured to capture a plurality of images of the blood cells as they pass through the flow cell; and d) one or more processors wherein the one or more processors: i) analyzing the plurality of images to determine whether review instructions apply to the plurality of images; ii) displaying an interface having the review instructions and a description of the review instructions; and iii) displaying an interface having at least one cell image corresponding to said review instructions. a sample analysis system programmed to perform actions including:

2. 2. The sample analysis system of claim 1, wherein the one or more processors are configured to determine that user-defined review conditions are satisfied and, in response to determining that the user-defined review conditions are satisfied, display the interface with the review instructions.

3. The sample analysis system of claim 1 , wherein the review indication is at least one of a high count indication or a low count indication.

4. The sample analysis system of claim 1 , wherein the one or more processors are configured to determine that the review instructions should be displayed based on satisfaction of built-in review conditions.

5. 10. The sample analysis system of claim 1, wherein the one or more processors are configured to determine that the review instructions should be displayed based on at least one of a low confidence condition being satisfied and a linearity condition not being satisfied.

6. 10. The sample analysis system of claim 1, wherein the one or more processors are programmed to determine that the review instructions should be displayed based on detecting at least one of platelet clumps or red blood cell clumps within the plurality of images of blood cells.

7. 10. The sample analysis system of claim 1, wherein the one or more processors are programmed to determine that the review instructions should be displayed based on detecting at least one of red blood cell fragments, sickle red blood cells, biphasic red blood cells, large platelets, giant platelets, reticulocytes, atypical lymphocytes, or blast cells within the plurality of images of blood cells.

8. The sample analysis system of claim 1 , wherein the interface includes a plurality of review instructions, the interface displaying a description of each review instruction and at least one cell image corresponding to each review instruction.

9. 10. The sample analysis system of claim 1, further comprising a non-transitory computer-readable medium having stored thereon a machine learning algorithm trained to analyze the images from the plurality of images and classify particles shown in the images, and wherein the one or more processors are programmed to determine that the review instructions apply to the plurality of images based on a confidence score provided by the machine learning algorithm for the classification of particles shown in the plurality of images.

10. 10. The sample analysis system of claim 1, further comprising a non-transitory computer-readable medium that stores a plurality of conditions for determining whether corresponding review instructions should be provided, the plurality of conditions including a set of user-defined conditions that are modifiable by a user of the sample analysis system and a set of built-in conditions that are not modifiable by a user of the sample analysis system.

11. a) each user defined condition from the set of user defined conditions is associated with a particular cell type; b) the set of user-defined conditions includes a first set of high and low thresholds for a particular cell type and a second set of high and low thresholds for the particular cell type; c) the one or more processors: i) determining that a first review instruction is applied to the plurality of images when the count of the particular cell type falls outside the first set of high and low thresholds and falls within the second set of high and low thresholds; ii) determining that a second review instruction is to be applied to the plurality of images when the count for the particular cell type falls outside the second set of high and low thresholds; 11. The sample analysis system of claim 10, wherein: d) the first and second review instructions are visually distinguishable from one another.

12. The sample analysis system of claim 11 , wherein the first and second review instructions have different colors.

13. 2. The sample analysis system of claim 1, wherein the at least one cell image corresponding to the review instruction includes a thumbnail cell image, and the one or more processors are programmed to display a full-resolution image of a blood cell captured by the image capture device corresponding to the thumbnail cell image in response to receiving a signal indicating user selection of the thumbnail cell image.

14. 2. The sample analysis system of claim 1, wherein the at least one cell image corresponding to the review instruction includes a plurality of thumbnail cell images corresponding to the review instruction, and the plurality of thumbnail cell images corresponding to the review instruction are sorted based on their respective contributions to the review instruction.

15. a) the one or more processors: i) a first processor programmed to analyze the plurality of images to determine whether the review instructions apply to the plurality of images; and ii) a second processor programmed to display the interface; b) the second processor is configured by an analyzer that also includes the flow cell and the fluidics system; 2. The sample analysis system of claim 1, wherein the first processor is not configured by the analyzer, is separated from the second analyzer by a wide area network, and communicates with the second processor via the wide area network.

16. 1. A method of analyzing a sample comprising: a) using a fluidics system that flows a portion of the sample through a flow cell; b) using an image capture device that captures multiple images of the blood cells as they pass through the flow cell; c) using one or more processors wherein the one or more processors: i) analyzing the plurality of images to determine whether review instructions apply to the plurality of images; ii) displaying an interface having the review instructions and a description of the review instructions; iii) displaying the interface with at least one cell image corresponding to the review instruction. A method for analyzing a sample, comprising:

17. 17. The method of claim 16, further comprising determining that user-defined review conditions are satisfied, and wherein displaying the interface is performed in response to determining that the user-defined review conditions are satisfied.

18. 17. The method of claim 16, wherein the review indication is at least one of a high count indication or a low count indication.

19. 17. The method of sample analysis of claim 16, wherein analyzing the plurality of images to determine whether the review instructions apply to the plurality of images comprises determining that the review instructions should be displayed based on satisfaction of built-in review conditions.

20. 17. The method of claim 16, wherein analyzing the plurality of images to determine whether the review instruction applies to the plurality of images comprises determining that the review instruction should be displayed based on at least one of a low confidence condition being satisfied and a linearity condition not being satisfied.

21. 17. The sample analysis method of claim 16, wherein analyzing the plurality of images to determine whether the review instruction applies to the plurality of images comprises determining that the review instruction should be displayed based on detecting at least one of platelet clumps or red blood cell clumps in the plurality of images of blood cells.

22. 17. The method of claim 16, wherein analyzing the plurality of images to determine whether the review instruction applies to the plurality of images comprises determining that the review instruction should be displayed based on detecting at least one of red blood cell fragments, sickle red blood cells, biphasic red blood cells, large platelets, giant platelets, reticulocytes, atypical lymphocytes, or blast cells within the plurality of images of blood cells.

23. 17. The method of claim 16, wherein the interface includes a plurality of review instructions, the interface displaying a description of each review instruction and at least one cell image corresponding to each review instruction.

24. Analyzing the plurality of images to determine if the review instructions apply to the plurality of images includes: a) analyzing the images from the plurality of images using a machine learning algorithm that is trained to analyze particles shown in the images; and 20. The method of claim 16, further comprising: b) determining that the review instructions apply to the plurality of images based on confidence scores provided by the machine learning algorithm for classifications of particles shown in the plurality of images.

25. 17. The sample analysis method of claim 16, wherein analyzing the plurality of images to determine whether the review instructions apply to the plurality of images includes retrieving from a non-transitory computer-readable medium a plurality of conditions for determining whether corresponding review instructions should be provided, the plurality of conditions including a set of user-defined conditions modifiable by a user of the sample analysis system and a set of built-in conditions not modifiable by a user of the sample analysis system.

26. a) each user defined condition from the set of user defined conditions is associated with a particular cell type; b) the set of user-defined conditions includes a first set of high and low thresholds for a particular cell type and a second set of high and low thresholds for the particular cell type; c) the method comprises: i) determining whether a first review instruction is applied to the plurality of images based on whether the count of the particular cell type falls outside the first set of high and low thresholds and within the second set of high and low thresholds; ii) determining whether a second review instruction is applied to the plurality of images based on whether the count for the particular cell type falls outside the second set of high and low thresholds; 26. The method of claim 25, wherein d) the first review instruction and the second review instruction are visually distinguishable from one another.

27. 27. The method of sample analysis of claim 26, wherein the first review instruction and the second review instruction have different colors.

28. a) the at least one cell image corresponding to the review instruction includes a thumbnail cell image; b) the method comprises: i) receiving a signal indicating a user selection of the thumbnail cell image; and ii) displaying a full resolution image of a blood cell captured by the image capture device corresponding to the thumbnail cell image in response to receiving a signal indicating user selection of the thumbnail cell image.

29. 17. The sample analysis method of claim 16, wherein the at least one cell image corresponding to the review instruction includes a plurality of thumbnail cell images corresponding to the review instruction, and the method includes sorting the plurality of thumbnail cell images corresponding to the review instruction based on their respective contributions to the review instruction.

30. a) the one or more processors: i) a first processor programmed to analyze the plurality of images to determine whether the review instructions apply to the plurality of images; and ii) a second processor programmed to display the interface; b) the second processor is configured by an analyzer that also includes the flow cell and the fluidics system; 17. The sample analysis method of claim 16, wherein c) the first processor is not configured with the analyzer, is separated from the second analyzer by a wide area network, and communicates with the second processor via the wide area network.

31. 1. A method of using a biological analyzer, comprising: a) using a fluidics system that flows a portion of the sample through a flow cell; b) using an image capture device that captures multiple images of the blood cells as they pass through the flow cell; c) observing review instructions associated with said sample; and d) reviewing the review instructions by accessing data corresponding to the review instructions through a user interface.

32. a) the review instructions associated with the sample are associated with at least a portion of the plurality of images; and 32. The method of claim 31, wherein b) reviewing the review instructions by accessing data corresponding to the review instructions through the user interface is performed by reviewing at least a subset of the at least portion.

33. a) the method comprises: i) viewing a set of thumbnails of cell images having a type corresponding to said review instruction; and ii) selecting a thumbnail from the set of thumbnails; 33. The method of claim 32, wherein b) reviewing the subset of the at least a portion of the plurality of images comprises viewing full resolution images corresponding to the selected thumbnails.

34. 34. The method of claim 33, comprising selecting a sorting criterion for the set of thumbnails of the cell images having the type corresponding to the review instruction.

35. a) accessing data corresponding to the review instructions includes reviewing messages indicating abnormal measurements derived from the plurality of images of blood cells; 33. The method of claim 32, wherein the method includes: b) determining whether the abnormal measurement derived from the plurality of images is correct based on reviewing further information corresponding to the abnormal result.

36. 36. The method of claim 35, wherein determining whether the abnormal measurement derived from the plurality of images is corrected based on reviewing further information corresponding to the abnormal result comprises observing one or more full resolution images from the plurality of images of blood cells.

37. 36. The method of claim 35, wherein determining whether the abnormal measurements derived from the plurality of images are corrected based on reviewing further information corresponding to the abnormal results comprises observing results derived by a non-imaging measurement system.

38. 38. The method of claim 37, wherein the anomaly measurements derived from the multiple images are counts for a type of cell and the results derived by the non-imaging measurement system are counts for the same type of cell.

39. 39. The method of claim 38, comprising determining whether to perform counting of the same type of cells using a new portion of the sample based on determining whether the abnormal measurements derived from the plurality of images are corrected.

40. 32. The method of claim 31, further comprising, for at least one cell type from the plurality of cell types, defining review conditions for that cell type.

41. 41. The method of claim 40, wherein the review conditions include multiple sets of thresholds, each set of thresholds including a high threshold and a low threshold.

42. 32. The method of claim 31, comprising determining that further analysis should be performed on the sample based on accessing the data corresponding to the review instructions through the user interface.

43. a) the further analysis includes capturing images of reticulocytes within the sample; b) the method includes the user accessing one or more of the images of reticulocytes; 43. The method of claim 42, wherein c) accessing the data corresponding to the review instructions through the user interface includes accessing a reticulocyte count for the sample.

44. a) accessing data corresponding to the review instructions includes reviewing a message indicating that a count for the sample based on the portion of the sample exceeds a maximum allowable count; 43. The method of claim 42, wherein b) the further analysis comprises redetermining the counts using a new portion of the sample.

45. 45. The method of claim 44, comprising diluting the new portion of the sample to a dilution level higher than the dilution level used for the portion of the sample that formed the basis of the count that exceeded the maximum allowable count.

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