Multi-channel hematological flow system

Abstract

A sample analysis system may include a flow imaging module, an alternative system, a fluidic system, and an analysis module. In such a system, a flow imaging module may include a flow cell and an image capture device, while an alternative system may include an impedance detector. The fluidic system may be adapted to flow a portion of the sample through the flow imaging system and alternative system. The analysis module may be used to determine values for a first plurality of parameters using data from the flow imaging module and values for a second plurality of parameters using data from the alternative system.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to and is a non-provisional patent application of provisional patent application 63 / 430,233, entitled “Multi-Channel Hematology Flow System,” filed in the United States Patent and Trademark Office on December 5, 2022. This application is hereby incorporated by reference in its entirety. Background Art

[0003] Hematology is one of the most commonly performed medical tests for providing an overview of a patient's health status. A blood sample can be extracted from a patient and stored in a test tube containing an anticoagulant to prevent blood clotting. Whole blood samples typically include three main categories of blood cells, including red blood cells (erythrocytes), white blood cells (leukocytes), and platelets (thrombocytes). Each category can be further divided into subcategories of members. For example, the five main types or subcategories of white blood cells (WBCs) have different shapes and functions. White blood cells can include neutrophils, lymphocytes, monocytes, eosinophils, and basophils. There are also subcategories of red blood cell types. The appearance of the particles in the sample can vary depending on pathological conditions, cell maturity, and other reasons. The red blood cell subcategory can include reticulocytes and nucleated red blood cells.

[0004] Conventional blood cell analysis techniques utilize principles such as impedance or the Coulter principle and fluorescence or light scattering to count and measure cells. These techniques utilize indirect measurements and may therefore be limited in the amount and quality of information they can provide. In addition, slide examination is a common secondary step in which test results require further analysis (e.g., to confirm the results or assess for abnormalities), which is typically accomplished through automated or manual slide imaging procedures.

[0005] Improvements to traditional blood cell analysis techniques are needed that utilize new technologies to optimize workflow and improve cell analysis to improve patient outcomes. Summary of the Invention

[0006] Described herein are apparatuses, systems, and methods for classifying objects, such as cells, using an analyzer that captures cell images (e.g., a bioanalyzer / bioanalysis system). In some embodiments, both images and additional values (e.g., impedance-derived values, bulk conductivity scatter-derived values, fluorescence-derived values, and / or spectrophotometric-derived values) of blood cells from a blood sample can be used in such classification or other types of analysis. In some embodiments, the image, image-derived values, and values derived from non-imaging techniques are presented on a user interface (e.g., a screen).

[0007] In some embodiments, cellular information obtained from an image and cellular information obtained by a non-imaging technique (e.g., impedance, fluorescence, or spectrophotometry) may overlap, for example, where imaging is used to obtain a first parameter of a first particle (e.g., red blood cell count or platelet count) and non-imaging is also used to obtain that parameter (e.g., red blood cell count or platelet count). In some embodiments, both values are presented on a user interface.

[0008] In some embodiments, a sample analysis system can be provided, which includes a fluid system and one or more processors. In such a system, the fluid system can be used to make the first portion of the blood sample flow through a first module, which is a flow imaging module including a flow cell and an image capture device, and the image capture device is configured to capture multiple images of the cells of the first portion of the blood sample. The fluid system can also be used to make the second portion of the blood sample flow through a second module, which is configured to test one or more numerical parameters of the cells of the second portion of the blood sample. One or more processors can be programmed to perform a set of actions. These actions can include determining one or more numerical parameters of the cells of the second portion of the blood sample, and presenting a computing interface that includes multiple images of the cells of the first portion of the blood sample and one or more numerical parameters of the cells of the second portion of the blood sample. Corresponding methods and computer-readable media can also be implemented based on the present disclosure. Therefore, the described system should be understood to be merely illustrative and should not be considered to impose limitations on the protection provided by this document or any related documents.

[0009] In some embodiments, an imaging system utilizes an image analysis algorithm to analyze cell images and report specific information about the cells—such as cell type, cell count, or other quantitative information about the cells. The algorithm can utilize, for example, a trained machine learning algorithm or pixel analysis to analyze the images.

[0010] In some embodiments, the bioanalysis system provides a check indication (e.g., a flag) associated with the analyzed biological sample. For example, the check indication can be associated with any of the following: a reported count of a particular cell type, an abnormal result, an abnormal cell type.

[0011] In some embodiments, a method of a bioanalysis system includes performing an image verification on a user interface, wherein a user can confirm a sample result through the user interface image verification. In some embodiments, the bioanalysis method includes analyzing a biological sample, presenting an image of cells of the biological sample on a user interface, and confirming the sample result through the user interface image verification. In some embodiments, the user interface image verification includes a verification indicator (e.g., a logo) associated with the analyzed biological sample.

[0012] In some embodiments, a multi-channel analyzer or multi-channel analysis system includes an imaging channel or module and one or more non-imaging channels. The one or more non-imaging channels utilize, for example, any of impedance, bulk conductivity scattering, fluorescence, or spectrophotometry.

[0013] In some embodiments, the methods of the embodiments described above and herein may be considered. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] While the specification concludes with claims particularly pointing out and distinctly claiming the invention, it will be appreciated that the invention will be better understood from the following description of certain examples taken in conjunction with the accompanying drawings, in which like reference numerals represent like elements, and in which:

[0015] Figure 1 is a schematic illustration showing, partially in cross-section and not to scale, operational aspects of an exemplary flow cell, autofocus system, and high optical resolution imaging apparatus for sample image analysis using digital image processing.

[0016] Figure 1A An optical bench apparatus according to various embodiments is shown.

[0017] Figure 1B Another optical bench apparatus is shown in accordance with various embodiments.

[0018] Figure 1C is a block diagram of a hematology analyzer according to various embodiments.

[0019] Figure 2 Aspects of a cell analysis system according to various embodiments are schematically depicted.

[0020] Figure 3 A system block diagram is provided that illustrates aspects of a cell analysis system according to various embodiments.

[0021] Figure 4 Aspects of an automated cell analysis system for assessing the leukocyte status of an individual are shown, according to embodiments of the present invention.

[0022] Figure 5 A process for deriving data from captured images and measured impedance is shown in accordance with various embodiments.

[0023] Figure 6 A process for verifying data derived from a captured image is shown in accordance with various embodiments.

[0024] Figure 7 Example user interfaces according to various implementations are provided.

[0025] Figure 8 Another process for deriving data from captured images and measured impedance is shown in accordance with various embodiments.

[0026] Figure 9 A module system is shown that may be used in some implementations of the disclosed technology.

[0027] Figure 10 shows a perspective view of an illustrative optical system of a fluorescence analyzer;

[0028] Figure 11 shows a process that can be used to stain a sample;

[0029] Figure 12 A system block diagram illustrating aspects of a cell analysis system according to an embodiment of the present invention is shown.

[0030] Figure 13 A spectrophotometric system used in some implementations of the disclosed technology is shown.

[0031] Figure 14 Shown Figure 13 How to use the spectrophotometric system.

[0032] Figure 15 A dual-channel test apparatus having an imaging system and a non-imaging system is shown;

[0033] Figure 16 Shown Figure 15 Schematic diagram of a non-imaging system;

[0034] Figure 17 Shown Figure 15 Imaging system;

[0035] Figure 18 shows the architecture of a machine learning model that can be used to analyze images; and

[0036] Figure 19 Such as can be included in the following Figure 18 An example of the stages in the architecture of a machine learning model.

[0037] The accompanying drawings are not intended to be limiting in any way, and it is contemplated that various embodiments of the invention may be implemented in a variety of other ways, including embodiments not necessarily depicted in the drawings. The accompanying drawings, which are incorporated in and form a part of the specification, illustrate several aspects of the invention and, together with the description, serve to explain the principles of the invention; it should be understood, however, that the invention is not limited to the precise arrangements shown. DETAILED DESCRIPTION

[0038] The present disclosure relates to devices, systems, compositions, and methods for analyzing samples containing particles. One embodiment may include an automated particle imaging system comprising an analyzer, which may be, for example, a visual analyzer. In some embodiments, the visual analyzer may further include a processor for facilitating automated analysis of the image.

[0039] Other embodiments may include other particle analysis systems and visual analyzers. These other particle analysis systems may include, for example, automated impedance measurement systems, fluorescence measurement systems, spectrophotometric systems, conductivity systems, light scattering systems, additional imaging systems, or other types of systems that can be used to collect data about a sample. In some embodiments, the analyzer may also include a processor to facilitate automatic analysis of images and / or present one or more interfaces that can present data from multiple channels (e.g., an interface that can present data derived from images captured by an imaging device and data derived from measurements performed by one or more of an impedance, conductivity, light scattering, fluorescence, or spectrophotometric system). In some embodiments, a bioanalyzer or bioanalysis system includes multiple channels or modules, including imaging channels / modules and one or more non-imaging channels / modules (e.g., impedance, conductivity, scattering, fluorescence, spectrophotometry).

[0040] Imaging system

[0041] According to some aspects of the present disclosure, a system including a visual / imaging analyzer or module for obtaining an image of a sample including particles suspended in a liquid can be provided. Such a system can be useful, for example, in characterizing particles in biological fluids (e.g., detecting and quantifying red blood cells, reticulocytes, nucleated red blood cells, platelets, and white blood cells, including white blood cell differential counting, classification and subclassification, and analysis). Other similar uses are also contemplated, such as characterizing blood cells from other fluids.

[0042] Although the identification and / or classification of blood cells in a blood sample is an exemplary application to which this theme is particularly suitable, other types of body fluid samples can be used. For example, various aspects of the disclosed technology can be used to analyze non-blood body fluid samples including blood cells (e.g., leukocytes and / or erythrocytes), such as serum, bone marrow, lavage fluid, exudate, transudate, cerebrospinal fluid, pleural effusion, peritoneal fluid, and amniotic fluid. The sample may also be a solid tissue sample (e.g., a biopsy sample that has been processed to produce a cell suspension). The sample may also be a suspension obtained by processing a fecal sample or a urine sample. The sample may also be a laboratory or production line sample including 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 fragment, portion, or aliquot thereof. In some processes, the sample may be diluted, divided into multiple parts, or stained.

[0043] In some aspects, sample is presented, imaged and analyzed in an automated manner. In the case of a blood sample, suitable diluent or saline solution can be used to significantly dilute the sample, which reduces the degree to which some views of cells may be hidden by other cells in the sample that is undiluted or less diluted. Cells can be processed using a reagent that enhances the contrast of certain cells, for example, using a permeabilizing agent to make the cell membrane permeable, and making histological stains adhere thereto and display features such as particles and nuclei. In some cases, it can be expected that aliquots of the sample are dyed, for counting and characterizing the particles comprising reticulocytes, nucleated red blood cells and platelets, and for leukocyte differential, characterization and analysis. In other cases, the sample comprising red blood cells can be diluted before introducing a flow cell and / or imaging in a flow cell or otherwise.

[0044] Now refer to Figure 1, shows a schematic example of a flow cell 22. In some embodiments, the flow cell 22 can transport a sample fluid through an observation area 23 of a high optical resolution imaging device 24, which is configured to image microscopic particles in a sample stream 32 using digital image processing. The flow cell 22 can be coupled to a source 25 of sample fluid, which may have been subjected to processing such as contact with a particulate contrast agent composition and heating. The flow cell 22 is also coupled to one or more sources of particle and / or intracellular organelle aligning fluid (PIOAL) 27, which is also referred to as sheath fluid, such as a clear glycerol solution having a viscosity greater than that of the sample fluid. In some embodiments, the PIOAL includes iminodicarboxylic acid, various salts, bromide, glycerol, and polyvinylpyrrolidone (PVP). Additional information regarding PIOAL / sheath fluids is provided in U.S. Patent No. 9,316,635, issued on April 19, 2016, entitled “Sheath fluid systems and methods for particle analysis in blood samples,” the disclosure of which is incorporated herein by reference in its entirety.

[0045] The sample fluid is injected through the flat opening at the distal end 28 of the sample feed tube 29 and enters the interior of the flow cell 22 at a point where the PIOAL flow is substantially established, resulting in a stable and symmetrical laminar flow of the PIOAL above and below (or on opposite sides) the ribbon-shaped sample stream 32. The sample and PIOAL flows can be supplied by a precision metering pump that moves the PIOAL along the substantially narrowed flow channel along with the injected sample fluid. The PIOAL envelops and compresses the sample fluid in the region 21 where the flow channel narrows. Thus, the reduction in flow channel thickness at region 21 can aid in the geometric focusing of the sample stream 32. The sample stream ribbon 32 is enveloping and carried along with the PIOAL downstream of the narrowed region 21, passing in front of or otherwise through the observation region 23 of the high optical resolution imaging device 24 where images are collected, for example, using a CCD 48. In this way, flow imaging is performed in which images are collected from a 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 stream flows into the discharge port 33 along with the PIOAL.

[0046] like Figure 1As shown, the narrowed region 21 can include a proximal flow channel portion 21a having a proximal thickness PT and a distal flow channel portion 21b having a distal thickness DT, with the distal thickness DT being less than the proximal thickness PT. Thus, a sample liquid can be injected through the distal end 28 of the sample tube 29 at a location distal from the proximal portion 21a and proximal to the distal portion 21b. Therefore, when the PIOAL flow is compressed by the region 21, the sample liquid can enter the PIOAL envelope. The sample liquid injection tube has a distal outlet port through which the sample liquid is injected into the flowing sheath fluid, and the distal outlet port is defined by the reduction in the flow channel dimension of the flow cell.

[0047] A digital high optical resolution imaging device 24 having an objective lens 46 is directed along an optical axis that intersects the strip-shaped sample flow 32. The relative distance between the objective lens 46 and the flow cell 22 can be varied by operation of a motor drive 54 for resolving and collecting focused digitized images on the photosensor array.

[0026] U.S. Patent No. 9,322,752, entitled "Flowcell Systems and Methods for Particle Analysis in Blood Samples," issued on April 26, 2016, provides information on such a device. Figure 1 Additional information regarding the construction and operation of the exemplary flow cell shown in FIG. 1 is provided in U.S. Pat. No. 9,857,361, entitled “Flowcell, Sheath Fluid and Autofocus Systems and Methods for Particle Analysis in Urine Samples,” issued on January 2, 2018, and is incorporated herein by reference in its entirety; Figure 1 The disclosure of U.S. Patent No. 5,836,794, is hereby incorporated by reference in its entirety for additional information on the construction and operation of the exemplary flow cell shown. Figure 1 The embodiment of represents a flow imaging system in which cells are imaged while flowing through a flow cell 22 .

[0048] Some embodiments may implement a technique for automatically achieving the correct operating position of the high optical resolution imaging device 24 to focus on the ribbon-like sample stream 32. The flow cell structure 22 may be configured such that the ribbon-like sample stream 32 maintains a fixed and reliable position within the flow cell, defining a flow path for the sample fluid, between layers of the PIOAL passing through the observation area 23 in the flow cell 22. In certain flow cell embodiments, the cross-section of the PIOAL's flow path narrows symmetrically at the point where the sample is inserted through a flat orifice (e.g., a tube 29 or cannula having a rectangular lumen at the orifice). The narrowed flow path (e.g., geometrically narrowed by a ratio of 20:1 in cross-sectional area or by a ratio between 20:1 and 70:1), along with the differential viscosity between the PIOAL and the sample fluid, and optionally the linear velocity difference between the PIOAL and the sample flow, collectively compresses 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.

[0049] In one aspect, the symmetry of the flow cell 22 and the injection pattern of the sample fluid and the PIOAL provide for a repeatable position of the ribbon-like sample stream 32 between the two layers of the PIOAL within the flow cell 22. Thus, process variations, such as the specific linear velocity of the sample and the PIOAL, do not tend to displace the ribbon-like sample stream from its position in the flow. Relative to the structure of the flow cell 22, the position of the ribbon-like sample stream 32 is stable and repeatable.

[0050] However, the relative positions of the flow cell 22 and the high optical resolution imaging device 24 of the optical system can vary and may benefit from occasional position adjustments to maintain an optimal or desired distance between the high optical resolution imaging device 24 and the ribbon sample stream 32, thereby providing high-quality focused images of envelope particles in the ribbon sample stream 32.

[0051] According to some embodiments, there may be an optimal or desired distance between the high optical resolution imaging device 24 and the ribbon-shaped sample stream 32 for obtaining a focused image of the enveloped particles. The optical device may first be accurately positioned relative to the flow cell 22 by autofocus or other techniques to position the high optical resolution imaging device 24 at the optimal or desired distance from an autofocus target 44 that 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 optical resolution imaging device 24 is then shifted at 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 optical resolution imaging device 24 is precisely focused on the ribbon-shaped sample stream 32 containing the enveloped particles.

[0052] Some embodiments may involve automatically focusing on a focal point or imaging target 44, which is a high contrast pattern that defines a known position along the optical axis of the high optical resolution imaging device or digital image capture device 24. The target 44 can have a known displacement distance relative to the position of the strip sample stream 32. A contrast measurement algorithm can be specifically employed for the target feature. In one example, the position of the high optical resolution imaging device 24 can be varied along a line parallel to the optical axis of the high optical resolution imaging device or digital image capture device to find the depth or distance at which one or more maximum differential amplitudes are found in pixel brightness values that occur along a line of pixels in the image that is known to pass through the edge of the contrast pattern. In some cases, the autofocus pattern does not vary along a line parallel to the optical axis, which is also the line along which the motorized control is operated to adjust the position of the high optical resolution imaging device 24 to provide the recorded displacement distance.

[0053] In this manner, there may be no need for autofocus or reliance on aspects of the image content that are variable between different images (that are less strictly defined in terms of contrast) or that may be located somewhere within a range of positions as a basis for determining a reference distance position. Having found the optimal or desired focus position on the autofocus target 44, the relative positions of the high optical resolution imaging device objective 24 and the flow cell 22 may be shifted according to the recorded displacement distance to provide the optimal or desired focus position for particles in the ribbon-like sample stream 32.

[0054] According to some embodiments, the high optical resolution imaging device 24 can resolve an image of the ribbon-like sample stream 32 as backlit by a light source 42 applied through an illumination opening (window) 43. Figure 1 In the embodiment shown, the perimeter of the illumination opening 43 forms an autofocus target 44. However, the aim is to collect a precisely focused image of the strip sample stream 32 by means of a high optical resolution imaging device optics 46 on an array of photosensors such as an integrated charge coupled device.

[0055] The high optical resolution imaging device 24 and its optics 46 are configured to resolve images of particles in the ribbon-shaped sample stream 32 focused at a distance 50, which may be a result of the size of the optical system, the shape of the lens, and the refractive index of its material. In some cases, the optimal or desired distance between the high optical 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 optical resolution imaging device and its optics 46 may change. Moving the high optical resolution imaging device 24 and / or the flow cell 22 closer to or farther away from each other (e.g., by adjusting the distance 50 between the imaging device 24 and the flow cell 22) causes the focal point at the end of the distance 50 to move relative to the position of the flow cell.

[0056] In some embodiments, the focusing target 44 can be located at a distance from the strip of 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 focusing target 44 is a constant displacement distance 52 from the strip of sample stream 32. Typically, the displacement distance 52 is constant because the position of the strip of sample stream 32 in the flow cell remains constant.

[0057] An exemplary autofocus process involves using motor 54 to adjust the relative position of high-optical-resolution imaging device 24 and flow cell 22 to achieve an appropriate focal distance, thereby focusing high-optical-resolution imaging device 24 on autofocus target 44. By way of example, the relative position adjustment is accomplished by moving one or more of imaging device 24, flow cell 22, or the imaging device's objective lens to change the relative position between imaging device 24 and flow cell 22. In this example, autofocus target 44 is positioned behind strip-shaped sample stream 32 in the flow cell. High-optical-resolution imaging device 24 is then moved toward or away from flow cell 22 until the autofocus process determines that the image resolved on the photosensor is an accurately focused image of autofocus target 44. Motor 54 is then operated to move the relative position of high-optical-resolution imaging device 24 and flow cell 22 to focus the high-optical-resolution imaging device on strip-shaped sample stream 32. Specifically, the high-optical-resolution imaging device 24 is precisely displaced away from flow cell 22 by a distance 52 to focus the high-optical-resolution imaging device on strip-shaped sample stream 32. In this exemplary embodiment, the imaging device 24 is shown as being moved by the motor 54 to reach a focused position. In another embodiment, the objective lens of the imaging device 24 is moved. In other embodiments, the flow cell 22 is moved in a similar manner, or both the flow cell 22 and the imaging device 24 are moved, to obtain a focused image.

[0058] These movement directions would be reversed if the focusing target 44 were located on the front viewport window instead of the rear illumination window 43. In this case, the displacement distance would be the span between the strip sample stream 32 and the target 44 at the front viewport (not shown).

[0059] The displacement distance 52 is equal to the distance between the strip sample stream 32 and the autofocus target 44 along the optical axis of the high optical resolution imaging device 24, which can be established in a factory calibration step or by the user. Typically, once established, the displacement distance 52 does not change. Thermal expansion changes and vibrations can cause the precise position of the high optical resolution imaging device 24 and the flow cell 22 to change relative to each other, requiring the autofocus process to be restarted. However, autofocusing on the target 44 provides a position reference that is fixed relative to the flow cell 22 and therefore fixed relative to the strip sample stream 32. Likewise, the displacement distance is constant. Therefore, by autofocusing on the target 44 and shifting the high optical resolution imaging device 24 and the flow cell 22 by the span of the displacement distance, the result is that the high optical resolution imaging device is focused on the strip sample stream 32.

[0060] In accordance with some embodiments, the focus target 44 is provided as a high contrast circle printed or applied around the illumination opening 43. Alternative focus target configurations are discussed elsewhere herein. When a square or rectangular image is collected in focus on the target 44, a high contrast border appears around the center of illumination. Finding the location in the image at the inner edge of the opening where the highest contrast is achieved automatically focuses the high optical resolution imaging device 24 at a working position on the target 44. In accordance with some embodiments, the term "working distance" may refer to the distance between the objective lens and its focal plane, and the term "working position" may refer to the focal plane of the imaging device. The highest contrast measurement of the image is where the brightest white and darkest black measurement pixels are adjacent to each other along a line passing through the inner edge. The highest contrast measurement can be used to estimate whether the focal plane of the imaging device 24 is in a desired position relative to the target 44.

[0061] Other autofocus techniques may also be used, such as edge detection techniques, image segmentation, and integrating the amplitude differences between adjacent pixels and finding the maximum sum of the differences. In one technique, the sum of the differences is calculated at three distances including the working position on either side of the target 44 and the resulting value is matched to a characteristic curve, where the optimal distance is at the peak on the curve. Relatedly, an exemplary autofocus technique may involve collecting images of the flow cell target at different positions and analyzing the images using a metric where the image of the target is sharpest to find the optimal focus position. During a first step (e.g., a coarse step), the autofocus technique may operate to find a preliminary optimal position from a set of images collected at 2.5 μm intervals. Starting from this position, the autofocus technique may then involve collecting a second set of images (fine) at 0.5 μm intervals and calculating the final optimal focus position on the target.

[0062] In some cases, the focusing target 44 (e.g., an autofocus pattern) can be located at the periphery of the field of view where the sample will appear. The focusing target 44 may also be defined by contrasting shapes located in the field of view. Typically, the autofocus target 44 is disposed on the flow cell 22 or is 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., a processor 18), the device automatically focuses on the target 44 rather than the strip of sample stream in response to maximizing the contrast of the image of the autofocus target. The focal plane of the working position or high optical resolution imaging device is then shifted from the autofocus target to the strip of sample stream by shifting the flow cell 22 and / or the high optical resolution imaging device 24 relative to each other by a displacement distance known as the distance between the autofocus target 44 and the strip of sample stream 32. As a result, the strip of sample stream 32 appears in focus in the collected digital image.

[0063] In some embodiments, an additional focusing step is used after the target autofocus step. For example, focusing on the target is a first step to establish an approximate position of the camera device relative to the flow cell / target of the flow cell. The additional step can utilize real-time focusing of the imaged sample (e.g., blood cells). One example includes pixel binning analysis between V / brightness values of red blood cells or white blood cells and comparison of V values between various bins to establish an ideal focus position. Alternatively, after using the target to set the position of the camera device relative to the flow cell / target of the flow cell, a focus assessment step that measures the focus quality after image acquisition can be performed to monitor changes in the camera focus position over time - for example, using V / brightness values of red blood cells or white blood cells as described herein. Additional information regarding autofocus methods that may be implemented in some embodiments is provided in U.S. Patent 9,857,361, U.S. Patent 10,705,008, U.S. Patent 10,705,011, International Patent Application PCT / US2022 / 052702, and International Patent Application PCT / US2023 / 011759, the contents of each of which are hereby incorporated by reference in their entirety.

[0064] In order to differentiate between classes and / or subclasses of particle types, such as red blood cells and white blood cells, by data processing techniques, it is advantageous to record microscopic pixel images with sufficient resolution and clarity to reveal aspects that distinguish one class or subclass from other classes.

[0065] In an embodiment, the device may be based on Figure 1A As shown in Figure 1BThe optical bench apparatus is a medium magnification apparatus having an illumination source 42 directed toward the flow cell 22 mounted in a gimbal or flow cell carrier 55 to backlight the contents of the flow cell 22 in images obtained by the high optical resolution imaging device 24. The carrier 55 is mounted on a motor drive so that it can be precisely moved toward and away from the high optical resolution imaging device 24. The carrier 55 also allows for precise alignment of the flow cell 22 relative to the optical viewing axis of the high optical resolution imaging device or digital image capture device 24 so that the ribbon sample flow is in the region where the ribbon sample flow is imaged (i.e., as shown in FIG. 2 ). Figure 1 The flow is depicted in a plane perpendicular to the viewing axis) between the illumination opening 43 and the viewing port 57. The focusing target 44 can help adjust the carrier 55, for example, to establish a plane of ribbon-like sample flow perpendicular to the optical axis of a high optical resolution imaging device or digital image capture device.

[0066] 24 or image capture device object lens.As shown here, carrier 55 can include two pivot points 55a and 55b, so that carrier and flow cell 22 are adjusted in angle relative to image capture device 24. Angle adjustment pivot points 55a and 55b can be located in the same plane and centered around flow cell 22 channels (e.g., at image capture site). This allows for angular adjustment without causing any linear translation of flow cell 22 positions. Carrier 55 can rotate around the axis of pivot point 55a or around the axis of pivot point 55b or around two axes. Such rotation can be controlled by processor 18 and flow cell motion control mechanism (e.g., motor 54).

[0067] Continue to refer to Figure 1B , it can be seen that either or both of the image capture device 24 and / or the carrier 55 (along with the flow cell 22) can be rotated or translated in three dimensions along various axes (e.g., X, Y, Z). Thus, in some embodiments, techniques for adjusting the focus of the image capture device can include effecting axial rotation of the image capture device 24 about the imaging axis, such as by rotating the device about axis X. In another embodiment, focus adjustment can also be effected by axial rotation of the flow cell 22 and / or the carrier 55 about an axis extending along the imaging axis (e.g., about axis X) and within the field of view of the imaging device 24.

[0068] In some cases, the focus adjustment may include a rotation of the tip of the image capture device (e.g., a rotation about axis Y). In other cases, the focus adjustment may include a rotation of the tip of the flow cell 22 (e.g., a rotation about axis Y or about pivot point 55a). As depicted 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 may include a tilt rotation of the image capture device (e.g., a rotation about axis Z). In other cases, the focus adjustment may include a tilt rotation of the flow cell 22 (e.g., a rotation about axis Z or about pivot point 55b). As depicted here, pivot point 55a corresponds to the Y axis extending along and within the flow path of the flow cell. Figure 1B As shown, pivot point 55b corresponds to the Z axis that crosses the flow channel and the imaging axis. In some cases, the image capture device 24 can be focused on the sample flow by enabling the rotation of the flow cell 22 (e.g., around axis X) so that the rotation is centered on the field of view of the image capture device. The three-dimensional rotation adjustment described herein can be implemented to take into account position drift in one or more components of the analyzer system. In some embodiments, three-dimensional rotation adjustment can be implemented to take into account temperature fluctuations in one or more components of the analyzer system. In other embodiments, the adjustment of the analyzer system may include translating the imaging device 24 along axis X. In addition, in some embodiments, the adjustment of the analyzer system may include translating the carrier 55 or the flow cell 22 along axis X. Additional information about such a carrier that can be used in some embodiments is provided in U.S. patent application Ser. No. 18 / 224,953, the disclosure of which is incorporated herein by reference in its entirety.

[0069] Thus, according to one or more embodiments disclosed herein, a visual analyzer for obtaining an image of a sample comprising particles suspended in a liquid includes a flow cell 22 such as Figure 1 The flow cell 22 is coupled to a source 25 of sample and to a source 27 of PIOAL material. The flow cell 22 can define an internal flow channel that narrows symmetrically in the direction of flow. The flow cell 22 is configured to direct a flow 32 of sample enveloped by the PIOAL through an observation region in the flow cell, i.e., behind the observation port 57. In addition, referring again to Figure 1 , a digital high optical resolution imaging device 24 having an objective lens 46 can be directed along an optical axis that intersects the strip-shaped sample stream 32. The relative distance between the objective lens 46 and the flow cell 22 is varied by operation of a motor drive 54 for resolving and collecting a focused digitized image on the photosensor array.

[0070] 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 strip-shaped sample stream 32. In the embodiment shown, the autofocus target 44 is applied directly to the flow cell 22 at a position visible in images collected by the high optical resolution imaging device 24. In another embodiment, the autofocus target, if not applied directly to the body of the flow cell in a unitary manner, can be carried on a portion that is rigidly fixed in position relative to the flow cell 22 and the strip-shaped sample stream 32 therein.

[0071] The light source 42 can be a steady source or can be a flash that flashes in time with the operation of the high optical resolution imaging device photosensor, which is configured to illuminate the strip sample stream 32 and also contribute to the contrast of the target 44. In the depicted embodiment, the illumination comes from backlighting. In some examples, the light source 42 can include a single lamp (e.g., an LED) or multiple lamps (e.g., three LEDs - one green, one red, and one blue, which are combined to produce a single white light). Additional information about how illumination can be provided in some implementations is provided in U.S. patent application Ser. No. 18 / 224,937, the disclosure of which is hereby incorporated by reference in its entirety.

[0072] Now refer to Figure 1C, a block diagram illustrating additional aspects of a hematology analyzer 100c. In some embodiments and as shown, the analyzer 100c can include at least one digital processor 18 coupled to operate a motor driver 54 and analyze digitized images from the photosensor array collected at different focus positions relative to a target autofocus pattern 44. The processor 18 is configured to determine a focus position of the autofocus pattern 44 (e.g., to automatically focus on the target autofocus pattern 44 and thereby establish an optimal distance between the high optical resolution imaging device 24 and the autofocus pattern 44). In some embodiments, this can be achieved through an image processing step, such as applying an algorithm to evaluate the contrast level of the image at a first distance, which can be applied to the entire image or at least the edge of the autofocus pattern 44. The processor moves the motor 54 to another position and 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 the autofocus pattern 44 (or, if moved to that position, would optimize the accuracy of focus). The processor 18 may rely on a fixed spacing between the autofocus target 44 and the strip sample stream 32. The processor 18 may then control the motor 54 to move the high optical resolution imaging device 24 to the correct distance to focus on the strip sample stream 32. More specifically, the processor 18 may operate the motor 54 to shift the distance 50 between the high optical resolution imaging device 24 and the strip sample stream 32 by a displacement distance 52 (e.g., as shown in FIG. 5 ) that the strip sample stream is displaced from the target autofocus pattern 44. Figure 1 In this way, the high optical resolution imaging device is focused on the ribbon-like sample stream.

[0073] The internal contours of the flow cell, as well as the PIOAL and sample flow rates, can be adjusted so that the sample forms a ribbon-like stream 32. The stream can be approximately as fine as, or even finer than, the particles enclosed in the ribbon-like sample stream. Leukocytes can have a diameter of, for example, approximately 10 μm. By providing a ribbon-like sample stream 32 having a thickness less than 10 μm, the cells can be oriented when the ribbon-like sample stream is stretched by the sheath fluid or PIOAL. Surprisingly, stretching the ribbon-like sample stream along a narrowed flow channel within a PIOAL layer having a different viscosity (e.g., a higher viscosity) than the ribbon-like sample stream advantageously tends to align non-spherical particles in a plane substantially parallel to the flow direction and exerts forces on the cells, improving the focused content of the cells' intracellular structures. The optical axis of the high optical resolution imaging device 24 is substantially perpendicular (e.g., orthogonal) to the plane of the ribbon-like sample stream 32. The linear velocity of the ribbon-like sample stream 32 at the imaging point can be, for example, 20 mm / s to 200 mm / s. In some embodiments, the linear velocity of the ribbon-shaped sample stream can be, for example, 50 mm / sec to 150 mm / sec.

[0074] The thickness of the sample strip can be affected by the relative viscosity and flow rate of the sample liquid and PIOAL. Figure 1 , a source 25 of sample and / or a source 27 of PIOAL, for example comprising a precision displacement pump and / or optimized restrictor tubing dimensions, and a single fluid source for driving the associated fluid flows can be configured to provide the sample and / or PIOAL at a controllable and optimized flow rate to optimize the size of the ribbon sample stream 32, i.e., a thin ribbon at least as wide as the field of view of the high optical resolution imaging device 24. More information on methods for sample actuation that may be used in some embodiments is provided in International Patent Application PCT / US2002 / 054240, the disclosure of which is incorporated herein by reference in its entirety. In one example, the PIOAL is contained in a single canister having two flow channels - a first flow channel that delivers the PIOAL to a flow cell, and a second flow channel that delivers the PIOAL to a sample sample entry point near the flow cell, where the PIOAL is then used to push the sample sample into the flow cell. Restrictors were configured on each flow channel to influence the relative velocity / flow rate in each flow channel, and the use of a single PIOAL source ensured a relatively constant velocity / flow rate ratio between the sample and the PIOAL flow.

[0075] In one embodiment, the PIOAL source 27 is configured to provide PIOAL of a predetermined viscosity. This viscosity can be different from, and even 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 coordinated to maintain the ribbon-like sample stream at a displacement distance from the autofocus pattern and with predetermined dimensional characteristics, such as a favorable ribbon-like sample stream thickness. In another embodiment, the PIOAL can have a higher linear velocity and a higher viscosity than the sample, thereby stretching the sample into a flat ribbon. In some cases, the PIOAL viscosity can be as high as 10 centipoise.

[0076] exist Figure 1CIn the embodiment shown, the same digital processor 18 that is used to analyze the pixelated digital images obtained from the photosensor array can also be used to control the autofocus motor 54. However, typically the high optical resolution imaging device 24 does not autofocus for every image captured. The autofocus process can be performed periodically (at the beginning of the day or at the beginning of a shift), or for example when a temperature or other process change is detected by an appropriate sensor or when image analysis detects that refocusing may be required. In some cases, the automatic autofocus process can be performed for a duration of about 10 seconds. In some cases, the autofocus procedure can be performed before processing a rack of samples (e.g., 10 samples per rack). In other embodiments, the hematology image analysis can also be performed by one processor, and a separate processor (optionally associated with its own photosensor array) is arranged to handle the step of autofocusing to the fixed target 44.

[0077] The digital processor 18 can be configured to automatically focus at a programmed time or under programmed conditions or upon user demand, and further configured to perform image-based classification and sub-classification of particles. Exemplary particles include cells, white blood cells, red blood cells, etc. In one embodiment, the digital processor 18 is configured to detect an autofocus restart signal. The autofocus restart signal can be triggered by a detected temperature change, a drop in focus quality identified by a pixel image date parameter, the passage of time, or user input. Advantageously, upon measuring Figure 1 No recalibration is required in the sense that the displacement distance 52 depicted in FIG. 1 is recalibrated. Alternatively, the autofocus may be programmed to recalibrate at a certain frequency / interval between runs for quality control and or to maintain focus.

[0078] The displacement distance 52 varies slightly between different flow cells, but remains constant for a given flow cell. As a setup process when equipping an image analyzer with a flow cell, the displacement distance is first estimated, and then during a calibration step performed for autofocus and imaging, the exact displacement distance of the flow cell is determined and entered as a constant into the programming of the processor 18. In other embodiments, the processor 18 can present various information on the display 63 for user review and / or analysis, as will be discussed further herein.

[0079] As mentioned 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 combination with other suitable components, are configured to utilize a sample fluid (e.g., a patient sample) so as to use digital image processing to collaboratively (A) collect high-quality images of microscopic particles in the sample stream 32, (B) record such collected images, and (C) process the collected digital images (e.g., to classify such microscopic particles into various suitable categories and / or subcategories) using appropriate data processing techniques as will be apparent to one skilled in the art in view of the teachings herein. In other words, an imaging system / module similar to that described above can be used to obtain information about the sample fluid via high-quality images of microscopic particles within the sample fluid. For example, static or slide-based imaging can be used in place of the concepts of flow-based imaging and flow cell imaging described above and herein.

[0080] Imaging system combined with alternative systems

[0081] In addition to the imaging-based systems and modules described herein, some systems / modules may also obtain information from the sample fluid via means other than capturing high-quality images of microscopic particles in the sample stream 32. Such systems / modules may utilize, for example, impedance systems, fluorescence systems, light scattering systems, VCS systems (integrating volume, conductivity, and scattering), spectrophotometric systems, or any other suitable system that will be apparent to those skilled in the art in view of the teachings herein. Such systems may be referred to as alternative systems or "non-imaging" because these systems may not be able to capture high-quality images of microscopic particles. Some alternative systems may include systems that utilize different imaging analysis processes (e.g., different from flow imaging described herein) to obtain data, etc. Alternative systems can collect sample fluid information that includes the same, similar, and / or different parameters than the information obtained by the imaging systems described above.

[0082] These alternative systems can be helpful in obtaining certain particle information that may be difficult to derive from images. For example, an imaging system may not be able to assess volumetric data associated with cells, and therefore an alternative system may need to be included with the imaging system to establish this volumetric data. In another example, an imaging system may not be able to assess hemoglobin content from images, and therefore a separate hemoglobin module (e.g., a spectrophotometer) is included as an additional module. These alternative systems can 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).

[0083] In some embodiments, an analyzer or analysis 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 scattering, or volume conductivity scattering (VCS). Each channel can also be considered a module, such that there is an imaging module and one or more non-imaging modules. In one example, an analyzer or analysis system utilizes a flow imaging channel / module, an impedance channel / module, and a spectrophotometry channel / module.

[0084] 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 are dedicated imaging channels and dedicated non-imaging channels, where all non-imaging analysis is performed on a specific channel. In one example, an analyzer or analysis system utilizes two channels (a first flow imaging channel and a second non-imaging channel), and the second non-imaging channel utilizes multiple non-imaging modules including, for example, impedance and spectrophotometry modules. Additional descriptions of these alternative or non-imaging modules, channels, or systems are provided herein.

[0085] Impedance system

[0086] Now refer to Figure 2 , a schematic representation of a cell analysis system 200 is shown. In some embodiments and as shown, the system 200 can include a preparation system 210, a transducer module 220, and an analysis system 230. Although the system 200 is described herein at a very high level with reference to three core system blocks (e.g., 210, 220, and 230), those skilled in the art will readily appreciate that the system 200 includes many other system components (e.g., as described above with reference to FIG. Figure 1 、 Figure 1B and Figure 1C, a central control processor, a display system, a fluid system, a temperature control system, a user safety control system, and the like. In operation, a fluid sample (e.g., a whole blood sample (WBS)) 240 can be provided 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 of skill in the art. After aspiration, the sample 240 can be transported to the preparation system 210. The preparation system 210 receives the sample 240 and can 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 to provide to the transducer module 220. The preparation system 210 can also include a mixing chamber so that appropriate reagents can be added to the aliquots. For example, where the aliquots are to be tested to distinguish between white blood cell subsets, a lysis reagent (e.g., ERYTHROLYSE, an erythrocyte lysis buffer) can be added to the aliquots to break down and remove the red blood cells (RBCs). The preparation system 210 may also include a temperature control component (not shown) to control the temperature of the reagents and / or mixing chamber. Appropriate temperature control can improve the consistency of operation of the preparation system 210. As discussed elsewhere herein, sample data such as light scattering data, light absorption data, and / or current data can be obtained (e.g., using a transducer) and processed or used to determine various blood cell status indicators for an individual patient.

[0087] In some cases, a predefined aliquot can be transferred from the preparation system 210 to the transducer module 220. As described in further detail below, the transducer module 220 can be capable of performing direct current (DC) impedance, radio frequency (RF) conductivity, light transmission, and / or light scattering measurements on cells from the sample 240 as they pass through the transducer module 220 individually. The measured DC impedance, RF conductivity, and light propagation (e.g., light transmission, light scattering) parameters can be provided or sent to the analysis system 230 for data processing. In some cases, the analysis system 230 can include computer processing features and / or one or more modules or components, such as those described herein with reference to Figure 9 2 and those described further below, which can evaluate the measured parameters, identify and enumerate the blood cell composition, and correlate a subset of data characterizing the elements of the sample 240 with the white blood cell count (WBC) status of the individual. As shown therein, the cell analysis system 200 can generate or output a report 250 containing a predicted status and / or prescribed treatment plan for the individual. In some cases, excess biological sample from the transducer module 220 can be directed to an external (or alternatively internal) waste system 260.

[0088] In one embodiment, the transducer module 220 includes an impedance detector that uses impedance (also known as the Coulter principle) to count individual cells as they pass through the 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., with thresholds configured to count only cells within a range of red blood cells and platelets), mean corpuscular volume (mean volume of red blood cells), and mean platelet volume (mean volume of platelets).

[0089] exist Figure 3 Against the backdrop of Figure 3 Shown in greater detail in the transducer module (and with reference to the impedance portion of the transducer module), there are electrodes 334, 336 for performing DC impedance measurements on cells passing through the interrogation zone (e.g., two canisters separated by a hole through which the cells pass). Signals from the electrodes 334, 336 are sent to an analysis system 304 to process the data and establish a cell count and other numerical cell parameters (e.g., volume data). This data is then output to a report 306. Any remaining fluid is discharged to waste 308.

[0090] In one example, using only an impedance detector may have particular utility for counting red blood cells and platelets, or for counting white blood cells when there is no need to distinguish between various types of white blood cells. This is because it may be difficult to distinguish between various types of white blood cells (e.g., at least neutrophils, lymphocytes, monocytes, eosinophils, basophils) using impedance measurement alone, which will count the white blood cells and assess their size, but will require additional analysis to distinguish between the types of white blood cells. By way of example, an impedance detector can be used for one or more of the following: red blood cell count, platelet count, mean corpuscular volume, mean platelet volume, and / or white blood cell count.

[0091] Conductivity system

[0092] Figure 3 The transducer module including the conductivity measurement and associated more detailed components are shown in more detail. Note that Figure 3The figure illustrates how impedance (DC) measurements and conductivity can be integrated into a single system. In some embodiments and as shown, system 300 can include a transducer module 310 having a flow cell 330, which can include an electrode assembly having a first electrode 334 and a second electrode 336 to perform DC impedance and RF conductivity measurements on cells passing through a cell interrogation zone 332. Signals from electrodes 334 and 336 can be sent to an analysis system 304. 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 the cell wall acts as a conductor for high-frequency current, the 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 through the interior of each cell. The high-frequency current can be used to characterize the nuclear and granular composition of cells, as well as the chemical composition of the cell interior.

[0093] The wires or other transmission or connection mechanism can transmit signals from the electrode assembly (e.g., electrodes 334, 336) to the analysis system 304 for processing. For example, the 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 can include computer processing features and / or one or more modules or components, such as those described herein with reference to FIG. Figure 9 , which can evaluate measured parameters, identify and enumerate biological sample components, and associate a subset of data characterizing elements of the biological sample with the individual's status. As shown here, the cell analysis system 300 can generate or output a report 306 that contains a predicted status and / or a prescribed treatment plan for the individual. In some cases, excess biological sample from the transducer module 310 can be directed to an external (or alternatively internal) waste system 308. In some cases, the cell 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.

[0094] In some embodiments, the conductivity system may be standalone (eg, would not include an impedance detector), or may be paired with an impedance detector to provide additional particle information.

[0095] Light scattering system

[0096] Figure 4Aspects of an automated cell analysis system for predicting or assessing the type of white blood cells (WBCs) are shown. 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 can include an optical element 410 having a cell interrogation zone 412. The transducer also provides a flow channel 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 projected toward the cell interrogation zone 412, a volume of sheath fluid 424 can also enter the optical element 410 under pressure to uniformly surround the sample stream 422 and direct the sample stream 422 through the center of the cell interrogation zone 412, thereby achieving hydrodynamic focusing of the sample stream. In this manner, individual cells of the biological sample passing through the cell interrogation zone one at a time can be accurately analyzed.

[0097] Note that due to Figure 4 For purposes of illustration in the context of a biological sample, light scatter 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 the biological sample as they 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 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. Such conductivity measurements can provide information about the internal cellular contents of the cells. For example, high-frequency RF current can be used to analyze the nuclear and granular composition of individual cells passing through the cell interrogation zone, as well as the chemical composition of the cells' interior. Thus, in some embodiments, DC and RF measurements can be performed on cells passing through the cell interrogation zone. As previously discussed, 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 cases to provide additional cellular information (e.g., all non-imaging-based data) in a simplified configuration. Alternative embodiments may include light scattering itself (i.e., not including impedance or conductivity), or may include a combination of impedance and conductivity as a separate module added to the light scattering module. The principles of light scattering detection will now be further explained.

[0098] Now go to Figure 12As shown in this figure, the cell analysis system can include a transducer module 2910 having a light source or illumination source, such as a laser 2912, that emits a light beam 2914. The laser 2912 can be, for example, a 635 nm, 5 mW solid-state laser. In some cases, the system 2900 can include a focus alignment system 2920 that adjusts the light beam 2914 so that the resulting light beam 2922 is focused and positioned at a cell interrogation zone 2932 of a flow cell 2930. In some cases, the flow cell 2930 receives a sample aliquot from the preparation system 2902. Note that, as previously described, the light scattering detection system is also illustratively shown as having DC (impedance) and RF (conductivity), but can be a separate system or module.

[0099] In some cases, the aliquot is typically flowed through the cell interrogation zone 2932 such that its components pass through the cell interrogation zone 2932 one at a time. In some cases, the system 2900 may include a cell interrogation zone or transducer module or other features of a blood analysis instrument, 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, the cell interrogation zone 2932 may be defined by a square cross-section measuring approximately 50×50 microns and having a length of approximately 65 microns (measured in the direction of flow). The flow cell 2930 may include an electrode assembly having a first electrode 2934 and a second electrode 2936 to perform DC impedance and / or RF conductivity measurements on cells passing through the cell interrogation zone 2932. Signals from the electrodes 2934, 2936 may be sent 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 the cell wall acts as a conductor for high-frequency current, high-frequency current can be used to detect differences in the insulating properties of cellular components as current passes through the cell wall and through the interior of each cell. High-frequency current can be used to characterize nuclear and granular composition, as well as the chemical composition of the cell interior.

[0100] An incident light beam 2922 travels along a beam axis AX and illuminates cells passing through a cell interrogation zone 2932, causing light to propagate (e.g., be scattered, transmitted) within an angular range α emanating from the zone 2932. Exemplary systems are equipped with a sensor assembly that can detect light within three, four, five, or more angular ranges within the angular range α, including light associated with extinction or axial light loss measurements as described elsewhere herein. As shown here, light propagation 2940 can be detected by a light detection assembly 2950, which optionally includes a light scatter detector unit 2950A and a light scatter and transmission detector unit 2950B. In some cases, the light scatter detector unit 2950A includes a photosensitive region or sensor zone for detecting and measuring upper median angle light scatter (UMALS), for example, light scattered or otherwise propagated at angles ranging from about 20 degrees to about 42 degrees relative to the beam axis. In some cases, UMALS corresponds to light propagating within an angular range of from about 20 degrees to about 43 degrees relative to the axis of an incident light beam illuminating cells flowing through the interrogation zone. Light scatter detector unit 2950A can also include a photosensitive region or sensor area for detecting and measuring lower median angle light scatter (LMALS), for example, light scattered or otherwise propagating at angles within a range of from about 10 degrees to about 20 degrees relative to the beam axis. In some cases, LMALS corresponds to light propagating within an angular range of from about 9 degrees to about 19 degrees relative to the axis of an incident light beam illuminating cells flowing through the interrogation zone.

[0101] The combination of UMALS and LMALS is defined as median angle light scatter (MALS), which is light scatter or spread at angles between about 9 degrees and about 43 degrees relative to the axis of the incident light beam illuminating cells flowing through the interrogation zone.

[0102] like Figure 12As shown, light scatter detector unit 2950A can include an opening 2951 that enables low-angle light scatter or propagation 2940 to pass through 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 can include a photosensitive region or sensor area for detecting and measuring low-angle light scatter (LALS), for example, light scattered or propagated at an angle of approximately 5.1 degrees relative to the axis of the illumination beam. In some cases, LALS corresponds to light propagating at an angle of less than approximately 9 degrees relative to the axis of the incident light beam illuminating cells flowing through the interrogation zone. In some cases, LALS corresponds to light propagating at an angle of less than approximately 10 degrees relative to the axis of the incident light beam illuminating cells flowing through the interrogation zone. In some cases, LALS corresponds to light propagating at an angle of approximately 1.9 degrees ± 0.5 degrees relative to the axis of the incident light beam illuminating cells flowing through the interrogation zone. In some cases, LALS corresponds to light propagating at an angle of about 3.0 degrees ± 0.5 degrees relative to the axis of an incident light beam illuminating cells flowing through the interrogation zone. In some cases, LALS corresponds to light propagating at an angle of about 3.7 degrees ± 0.5 degrees relative to the axis of an incident light beam illuminating cells flowing through the interrogation zone. In some cases, LALS corresponds to light propagating at an angle of about 5.1 degrees ± 0.5 degrees relative to the axis of an incident light beam illuminating cells flowing through the interrogation zone. In some cases, LALS corresponds to light propagating at an angle of about 7.0 degrees ± 0.5 degrees relative to the axis of an incident light beam illuminating cells flowing through the interrogation zone.

[0103] According to some embodiments, the light scattering and transmission detector unit 2950B may include a photosensitive area or sensor zone for detecting and measuring light that is axially transmitted through the cell or propagated from the illuminated cell at an angle of 0 degrees relative to the axis of the incident light beam. In some cases, the photosensitive area or sensor zone can detect and measure light that is axially propagated from the cell at an angle of less than about 1 degree relative to the axis of the incident light beam. In some cases, the photosensitive area or sensor zone can detect and measure light that is axially propagated from the cell at an angle of less than about 0.5 degrees relative to the axis of the incident light beam. Such axial transmission or propagation of light measurement corresponds to axial light loss (ALL or AL2). As described in previously incorporated U.S. Patent No. 7,390,662, when light interacts with particles, some of the incident light changes direction through a scattering process (i.e., light scattering) and a portion of the light is absorbed by the particles. These two processes remove energy from the incident light beam. When viewed along the incident axis of the light beam, light loss can be referred to as forward extinction or axial light loss. Additional aspects of axial light loss measurement techniques are described in US Patent No. 7,390,662 at column 5, line 58 to column 6, line 4.

[0104] Thus, the cell analysis system 2900 provides a means for obtaining light propagation measurements, including light scattering and / or light transmission of light emitted from illuminated cells of a biological sample at any of a variety of angles or within a variety of angular ranges, including ALL and a plurality of different light scattering or propagation angles. For example, the light detection assembly 2950, including appropriate circuitry and / or processing units, provides a means for detecting and measuring UMALS, LMALS, LALS, MALS, and ALL.

[0105] Wires or other transmission or connection mechanisms can send signals from the electrode assembly (e.g., electrodes 2934, 2936), the light scattering detector unit 2950A, and / or the light scattering and transmission detector unit 2950B to the analysis system 2904 for processing. For example, the measured DC impedance, RF conductivity, light transmission, and / or light scattering parameters can be provided or sent to the analysis system 2904 for data processing. In some cases, the analysis system 2904 can 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 the composition of the biological sample, and associate a subset of the data representing the elements of the biological sample with the infection status of the individual. As shown here, the cell analysis system 2900 can generate or output a report 2906 that contains the assessed infection status and / or prescribed treatment plan for the individual. In some cases, excess biological sample from the transducer module 2910 can be directed to an external (or alternatively internal) waste system 2908. In some cases, the cell analysis system 2900 may include one or more features of a transducer module or blood analysis instrument, such as those described in previously incorporated US Patent Nos. 5,125,737; 6,228,652; 8,094,299; and 8,189,187.

[0106] Fluorescence system

[0107] Figure 10An illustrative flow cytometer 2000 is depicted that can be used with a fluorescence system to measure various parameters of a sample fluid, as will be apparent to those skilled in the art in light of the teachings herein. In some cases, cells from a blood sample are treated with a hemolytic agent to lyse the red blood cells, leaving white blood cells in the sample fluid. Additionally, the remaining white blood cells can then be stained with a fluorescent dye, which can alter the fluorescence intensity. Such a preparation procedure can utilize the teachings of the sample preparation process described herein. Where the white blood cells are appropriately stained as described herein, the sample fluid containing the stained cells can be introduced into the flow cytometer 2000 to measure the scattered light and fluorescence of each cell when the cells are illuminated by a laser.

[0108] 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 portion of a sheath flow cell 2023 via a collimating lens 2022. Simultaneously, particles (e.g., cells (e.g., blood cells or body fluid cells)) from the sample fluid enter the orifice portion of the sheath flow cell 2023 through a nozzle 2020. Thus, the particles are guided 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 portion of the flow cell, into which a prepared measurement sample has been introduced, with light that can excite a dye used in sample processing and is selected based on a fluorescent dye that stains particles (e.g., blood cells or body fluid cells) in the sample. Therefore, depending on the type of fluorescent dye used, in addition to semiconductor lasers, for example, red argon lasers, He-Nc lasers, and blue semiconductor lasers can also be used.

[0109] The forward scattered light emitted from the particles is introduced into a forward scattered photodetector 2026 (e.g., a photodiode) via a condenser lens 2024 and a pinhole plate 2025. Furthermore, the side scattered light emitted from the particles is introduced into a side scattered light detector 2029 (e.g., a photomultiplier tube) via a condenser lens 2027 and a dichroic mirror 2028. The side fluorescence emitted from the particles is also introduced into a side fluorescence detector 2031 (e.g., a photomultiplier tube) via a condenser lens 2027, a dichroic mirror 2028, an optical 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 fluorescence signal output from the side fluorescence detector 2031 are amplified by amplifiers 2032, 2033, and 2034, respectively, and input into the control unit 2006. The control unit 2006 analyzes these signals and calculates the received signal intensity. The control unit 2006 or any other suitable component of the fluorescence system can use these scattered light intensities to calculate and display appropriate measurement parameters, which will be apparent to those skilled in the art in view of the teachings herein. Additional information about fluorescence systems that can be applied to cell analysis in some embodiments is provided in U.S. Patents 7,625,730 and 7,892,841, the disclosures of each of which are hereby incorporated by reference in their entirety.

[0110] Note that fluorescence systems are sometimes referred to in the art as optical systems because they utilize laser excitation and use mirrors in a non-imaging setting, so fluorescence systems may also be referred to as optical systems.

[0111] Some fluorescence techniques may also utilize imaging as part of the analysis process (e.g., fluorescence in situ hybridization, also known as FISH). A fluorescence imaging module (e.g., FISH) may be used as part of an additional module for evaluating biological samples (e.g., blood cells) as a separate module from the flow imaging module previously described. 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 analysis system may include a flow imaging module (e.g., Figure 1 ) and fluorescence imaging modules - as separate imaging modules. Alternatively, the multi-module analysis system may include a flow imaging module (e.g., Figure 1 ) and a separate fluorescence module that may include a fluorescence imaging component. Alternatively, a multi-module analysis system may include an imaging module (e.g., Figure 1 flow imaging or FISH) and at least one separate module that does not utilize imaging (e.g., impedance, spectrophotometry, fluorescence cytometry, light scattering, or conductivity).

[0112] Spectrophotometer system

[0113] Figure 13 A spectrophotometer 3000 is shown that is operable to measure absorbance, transmittance, and / or other characteristics of a diluted and lysed blood sample—and to measure the red blood cell hemoglobin content—in one example, the hemoglobin concentration of the blood sample. The measured characteristics are then converted into corresponding measurements of hematological parameters.

[0114] The spectrophotometer includes a light source 3021a, a lens 3021b, a prism 3021c, a cuvette 3021d, and a detector 3021e. To obtain an absorbance or transmittance reading, a blood sample is passed through the cuvette, and the light source transmits light through the lens 3021b, the prism 3021c, the test tube 3021d, and the blood sample passing through. Detector 3021c, positioned on the opposite side of the cuvette 3021d, obtains the absorbance and / or transmittance reading of the blood sample. To convert the absorbance and / or transmittance reading of the blood sample into a hematological measurement, a lookup table can be used to associate the reading with the hematological measurement, or alternatively, the system can be programmed to perform this calculation. This is accomplished by processor 3024 and memory 3025.

[0115] exist Figure 13 In an embodiment, the processor 3024 and memory 3025 are included as part of an automated hematology analyzer. However, the processor 3024 and memory 3025 may also take a variety of different forms, such as a processor in a connected personal computer or other instrument that is operable to convert absorbance and / or transmittance readings into uncorrected hematology measurements, such as hemoglobin concentration. In this embodiment, the processor 3024 may be any commercially available microprocessor. The processor 3024, associated with the memory 3025, is further operable to acquire uncorrected hematology measurements and convert them into corrected hematology parameters, wherein the corrected hematology parameters are based on the uncorrected hematology measurements and the temperature measurements acquired by the temperature sensor 3017. Such corrected hematology measurements compensate for inaccuracies in the uncorrected hematology measurements due to temperature and provide a more accurate measurement of the hematology parameters measured in the blood sample.

[0116] Figure 14Once a blood sample 3102 is obtained and diluted and lysed 3104, it is shown that, in step 3108, the blood sample passes through a cuvette 3021d. As described above, in one embodiment, the cuvette 3021d is part of the spectrophotometer 3000 or other measuring instrument. In step 3110, the spectrophotometer 3000 obtains absorbance and / or transmittance measurements of the blood sample. In step 3112, these measurements are then passed to the processor 3024, where the hemoglobin measurement is determined by the processor 3024 based on the absorbance / transmittance measurements. In one embodiment, the processor 3024 uses a lookup table stored in the memory 3025 to determine the hemoglobin measurement, or is programmed to correlate the absorbance / transmittance measurements with the hemoglobin measurement. Specifically, to obtain the hemoglobin measurement, the processor 3024 simply uses the absorbance measurement obtained for the blood sample to obtain the corresponding hemoglobin measurement. The processor can then obtain the hemoglobin measurement in step 3112.

[0117] System implementation

[0118] Figure 15 An example of an imaging system and a non-imaging system combined into a test device 4000 is shown. Test device 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. Imaging system 4200 and non-imaging system 4100 may be in fluid communication with the SAM, such that the SAM can provide a fluid sample to both imaging system 4200 and non-imaging system 4100. In other words, imaging system 4200 receives a portion (e.g., a first portion) of a blood sample, and non-imaging system 4100 receives another portion (e.g., a second portion) of a blood sample (e.g., two different aliquots of the same blood sample, or an aliquot of the same blood sample divided into a first portion that enters imaging system 4200 and a second portion that enters non-imaging system 4100).

[0119] Figure 17 A detailed example of an imaging system 4200 of a test device 4000 is shown. 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, and any combination of RBC and WBC chambers may be present. Blood is separated into the RBC and WBC chambers, with the blood in the WBC chamber receiving additional reagents and preparations, as will be described herein.

[0120] The probe 4005 can be used to mix various fluid samples before use. Once mixed, the sample can be aspirated from the aspiration pump 4010 using vacuum at the probe 4005, and the probe can then be positioned sequentially in the RBC chamber 4215 and the two WBC chambers 4220, 4225, thereby delivering the first portion of the blood sample to the RBC 4215 and 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 lysis reagent (for lysis / removal of red blood cells), and a staining reagent (for staining the nuclear region of white blood cells). The segmented blood samples in the WBC chambers 4220, 4225 can then be mixed with a staining agent 4235 and a diluent 4240 and incubated in the chambers 4215, 4220, 4225 using an integrated heater. Because of the difficulty in distinguishing white blood cells, staining the nuclear region helps to better illustrate and display the nuclear region to aid in white blood cell differentiation (e.g., at least distinguishing neutrophils, lymphocytes, monocytes, eosinophils, and basophils). A lysing agent is used to eliminate red blood cells during this white blood cell analysis cycle.

[0121] In one embodiment, staining agent and cleavage agent are two separate compounds added during a separate deposition step. In one embodiment, staining agent and cleavage agent are in a composition comprising both staining agent and cleavage agent together, wherein the composition includes saponin, multiple staining agents (e.g., a combination of new methylene blue, crystal violet and basic fuchsin) and glutaraldehyde. Additional information about staining agent and cleavage agent compositions can be found in U.S. Patent No. 9,279,750 and U.S. Published Patent Application 2021 / 0108994, the disclosure of which is incorporated herein by reference in its entirety.

[0122] Once incubated, the blood can be transferred to a flow cell within the imaging component 4230 (e.g., Figure 1 22). Blood from the RBC chamber 4215 is imaged in one cycle. Note that since the RBC chamber does not receive staining reagents and lysing reagents, this cycle takes a shorter time. Blood from the WBC chambers 4220 and 4225 is imaged in different cycles (e.g., two separate cycles). Once entering the flow cell 4233 and entering the flow of the sheath 4245, the optical bench module (OBM) can capture the cell image and convert the full frame into an image block. After conversion, the image processing module (IPM) can pre-process and classify the image blocks. The classified image blocks can then be used to generate analysis data.

[0123] The sample portions remaining in chambers 4215, 4220, 4225 can then be transferred from their respective chambers to an alternative system (not shown) that can perform further measurements on the sample (e.g., to perform a different analytical test). Alternatively, any sample portions remaining in chambers 425, 4220, 4225 are flushed into a waste container 4250, and the chambers are cleaned (e.g., with a diluent) in preparation for receiving another blood sample. The sample portions that have been analyzed by imaging component 4230 and (optionally in an alternative system after the imaging step) can then be deposited into a waste container 4250, and the imaging system 4200 is cleaned (e.g., with a diluent) in preparation for a subsequent blood sample.

[0124] The inclusion of non-imaging system 4100 can be useful for a variety of reasons, including providing a secondary source of information to confirm results using more traditional blood analysis techniques or providing analysis of cellular parameters that may be difficult to assess via imaging—for example, volumetric data (such as mean corpuscular volume (MCV)) or the hemoglobin content of red blood cells. In some embodiments, system 4100, rather than a non-imaging system, can be an alternative system to perform supplemental imaging in another manner as an additional step to the flow imaging system of imaging system 4200. In various examples, the non-imaging system can include various combinations of impedance modules, conductivity modules, light scatter modules, volume conductivity scatter (VCS) modules, fluorescence modules, and spectrophotometric modules.

[0125] Figure 16 The non-imaging system 4100 is shown to include, among other components, a pair of fluid analysis chambers, including a first fluid analysis chamber in the form of a first trough 2212 and a second fluid analysis chamber in the form of a second trough 2214. The first trough 2212 is a reservoir for white blood cells (WBCs) or hemoglobin (HGB), and the second trough 2214 is a reservoir for red blood cells (RBCs). In the example shown, the WBC trough 2212 is open to allow the sample probe 4005 of the test device 4000 to selectively access the WBC trough 2212, for example, to aspirate fluid therefrom and / or dispense fluid therein. Although not shown, the WBC trough 2212 and the RBC trough 2214 of this embodiment can be accommodated within the confines of the non-imaging system 4100. The non-imaging system 4100 also includes a sweep canister 2241 in selective fluid communication with both troughs 2212 and 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 detergent reservoir 2233 containing a detergent CL.

[0126] The diluent reservoir 2230 is in fluid communication with the sweep tank 2241, the WBC groove (212), and the RBC groove 2214. In addition, the cleaning reservoir 2233 is in fluid communication with the sweep tank 2241, grooves 2212, 2214, and any other suitable components, which will be apparent to those skilled in the art in view of the teachings herein. The non-imaging system 4100 can deliver the diluent (D) from the diluent reservoir 2230 to the sweep tank 2241, the WBC groove 2212, and the RBC groove 2214 to appropriately dilute the sample as described herein. In some cases, the sweep tank 2241 can selectively receive the diluent (D) and the cleaning agent (CL) as described herein, and also communicate the fluids received in this manner to grooves 2212, 2214. It should also be understood that the tanks 2212, 2214 can also be in fluid communication with the reservoirs 2230, 2233, such that the tanks 2212, 2214 can directly receive the diluent (D) and the cleaning agent (CL).

[0127] The non-imaging system 4100 is configured to appropriately communicate with the cleaning agent (CL), tanks 2212, 2214, sweep tank 2241, and various other suitable components of the non-imaging system that will be apparent to those skilled in the art in view of the teachings herein. The cleaning agent (CL) can be distributed throughout the system 4100 to appropriately remove traces of previous samples processed by the system 4100.

[0128] Additionally, the lysing agent reservoir 2232 is in fluid communication with the WBC trough 2212. The non-imaging system 4100 is configured to deliver the lysing agent (L) from the lysing agent reservoir 2232 to the WBC trough 2212 to properly lyse the blood sample to properly remove red blood cells from the sample in the WBC trough 2212.

[0129] Troughs 2212, 2214 and / or sweep tank 2241 are also in appropriate communication with sample analyzer 2221 so that the sample fluid can be delivered to sample analyzer 2221 for appropriate analysis, as will be apparent to those skilled in the art in view of the teachings herein. Waste receptacle 2246 is in fluid communication with various components of system 4100 so that processed samples, diluents (D), detergents (CL), lysing agents (L), etc. used in conjunction with system 4100 can be appropriately disposed of after illustrative use.

[0130] 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, such that the non-imaging system 4100 can be referred to as a blood analysis system. Although not shown, the WBC tank 2212 of this embodiment can include a hemoglobin transducer configured to measure the amount of hemoglobin present in the fluid medium contained within the WBC tank 2212. For example, the hemoglobin transducer can 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 within the WBC tank 2212. In some embodiments, the WBC trough 2212 and the RBC trough 2214 can each be fluidically coupled to a suitable sample analyzer 2221 via corresponding input and output conduits equipped with corresponding valves to selectively transfer fluid media from one of the WBC trough 2212 or the RBC trough 2214 to the appropriate sample analyzer 2221 and / or return such fluid media from the sample analyzer 2221 to the WBC trough 2212 or the RBC trough 2214. The sample analyzer 2221 can be configured to measure any suitable parameter (e.g., complete blood cell count, etc.) of the fluid media received from each trough 2212, 2214, as will be apparent to those skilled in the art in view of the teachings herein. In other embodiments, only one of the WBC trough 2212 or the RBC trough 2214 (e.g., only the RBC trough 2214) can be fluidically coupled to the sample analyzer 2221. In the example shown, the sample analyzer 221 is also fluidically coupled to the pneumatic transducer 2222. Although analysis of blood is shown and described herein (e.g., impedance-based counting, optical techniques, and / or imaging), the bioanalysis system 2210 can analyze (and optionally image) a variety of fluids, including but not limited to other body fluids such as synovial fluid, urine, bone marrow, etc.

[0131] It should be understood that non-imaging system 4100 can include any other suitable components as would be apparent to one skilled in the art in view of the teachings herein. Thus, suitable fluid lines, pumps, valves, multi-flow units, etc. can be readily incorporated into non-imaging system 4100.

[0132] Return to reference Figure 5In some embodiments, a blood sample can be received in a test tube and / or obtained for testing 501, the blood sample including an identifier. For example, in some embodiments, the blood sample or blood sample container can include a barcode 4057, a QR code, a radio frequency identification (RFID), etc. The identifier can contain relevant details about the sample, such as, for example, patient information, time data associated with the sample, a desired test procedure, etc. Thus, in some embodiments, the system can automatically or with user assistance obtain the data contained in the identifier and determine 502 one or more tests for the sample.

[0133] Once the test is determined 502, in some embodiments, the system can capture 503 an image of the blood cells in the flow cell. Figure 1 、 Figure 1A and Figure 1B The flow imaging system shown in can be used to capture 503 images of blood cells as they pass through the flow pool. In addition to image capture 503, the system can also include an analysis system or transducer (e.g., 300 and 400) to measure 504 the impedance of the blood cells (e.g., an alternative system). Other types of measurement channels or modules can also be included, such as a fluorescence channel or a spectrophotometric channel. Using the measurements from these different channels (e.g., captured images and measured impedance), data related to the sample can be derived 505. The derived data can then be displayed 505 to the user or can be manipulated to evaluate the derived data. By way of non-limiting example, Table 1 shown below provides a non-exhaustive list of possible parameters about a sample that can be determined and / or derived using the disclosed technology.

[0134]

[0135]

[0136] Table 1

[0137] For the illustrative purposes of Table 1, most flow imaging-derived cellular data are associated with cell counts, and thus the data derived from the images are primarily counts. In other examples, quantitative data about individual cell types can be associated with flow imaging techniques—such as cell diameter or nuclear area of individual cells.

[0138] In some embodiments, the aliquoter can be configured to divide the sample into a plurality of aliquots such that each aliquot can be subjected to a separate analysis (e.g., an image-based analysis or an impedance-based analysis). Thus, it should be understood that, as discussed herein, the sample can be divided and passed to different modules for analysis. For example, in some embodiments, the analysis system can be configured to flow a first portion of the sample through a flow imaging module for imaging red blood cells (RBCs) while simultaneously passing another portion of the sample through a second flow cell for imaging white blood cells (WBCs).

[0139] Non-smear based image analysis

[0140] In another embodiment and as Figure 6 and Figure 7 As shown, the system may include a user interface that allows the user to assess potential outliers or errors in the analysis without the need for manual evaluation (e.g., smears). In other words, the user can confirm the results on the screen using images derived from flow imaging without the need for separate imaging using smear / slide samples, thereby saving a significant amount of time. Alternatively, the user can use the presented images to confirm that the cells are correctly labeled and / or confirm the presented results. In some cases, this type of functionality can be implemented using algorithms that analyze captured images and / or related data such as impedance measurements and identify issues that may require further investigation. Figure 6 An example of a method that may be implemented to allow a user to check such a question is shown in . In the method shown in the figure, initially, images may be captured 601 by an image capture device as they pass through a flow cell. Based on the captured 601 images and possibly other types of data (e.g., impedance measurements, fluorescence measurements, etc.), a processor (e.g., 18) may be able to generate 602 result data that includes parameters of the sample (e.g., those shown in Table 1). The system may then analyze the captured images, possibly in combination with other data, to determine 603 a check indication to provide to the user. This may be done, for example, using, for example Figure 18 The machine learning algorithm shown is completed by the machine learning algorithm that is trained to classify the particle images from blood cells into various cell classifications including normal cell types and abnormal cell types. Note that Figure 18An illustrative example of an architecture for a cell classifier for labeling cells is provided, and various types of models for this purpose can be used, such as neural networks, convolutional neural networks, and modified publicly available neural networks. Additional examples can utilize pixel analysis and masking techniques to determine cell classification. Additional information about techniques that can be used in some embodiments of cell classification can be found in U.S. Patent No. 11,403,751, the disclosure of which is incorporated herein by reference in its entirety.

[0141] In various embodiments, a single classifier is used to classify all cell types including abnormal cell types. In some embodiments, multiple classifiers can be used in conjunction with a voting protocol for providing a final classification of cell types. In some embodiments, multiple classifiers include classifiers specifically assigned to abnormal cell types or subsets of abnormal cell types (e.g., if a cell is classified as a red blood cell, the use of a classifier uniquely for identifying abnormal cell types associated with red blood cells can be triggered).

[0142] exist Figure 18 In the architecture of FIG, an input image 1801 is analyzed in a series of stages 1802a to 1802n, each of which may include one or more layers and which are Figure 19 As shown in more detail. Figure 19 As shown, input 1901 (which is in Figure 18 The initial layer 1902a of the image layer (which will be a cell image, otherwise it will be the output of the previous stage) is provided to stage 1902, where the input 1901 is processed by the convolutional layers of stage 1902 to generate one or more transformed images 1903a to 1903n. This processing may include convolving the input 1901 with a set of filters 1904a to 1904n, each of which will identify the type of feature from the underlying image, which will then be captured in the corresponding transformed image of that filter. For example, as a simple example, convolving the image with the filters shown in Table 2 can generate a transformed image that captures the edges from the input 1901.

[0143]

[0144] Table 2: Example convolution filters

[0145] like Figure 19As shown, in addition to generating the transformed images 1903a to 1903n, the stage may also include a pooling layer that generates a pooled image 1905a to 1905n for each of the transformed images 1903a to 1903n. This can be done, for example, by organizing the appropriate transformed images into a set of regions and then replacing the values in the region with a single value (e.g., the maximum value of the region or the average of the values in 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 region into which it is divided (e.g., if the transformed images 1903a to 1903n have N x N dimensions, and it is divided into 2x2 regions, then the pooled images 1905a to 1905n will have dimensions (N / 2)x(N / 2)). These pooled images 1905a to 1905n can then be combined into a single output image 1906, where each of the pooled images 1905a to 1905n is considered as a separate channel in the output image 1906. This output image 1906 can then be provided as input to the next stage 1902a to 1902n, as shown in Figure 18 shown.

[0146] Back to Figure 18 As discussed above, after a final output image 1803 is created through the various stages of processing 1802a through 1802n, the final output image 1803 can 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 can include, for example, various types of images that can be captured (e.g., WBCs, RBCs), including image types whose presence can trigger a verification indication (e.g., platelet clumps).

[0147] Exemplarily, a trained CNN may include the following layers:

[0148] i. Input layer, which receives a 128x128x3 RGB image depicting a red blood cell image, followed by

[0149] ii. A convolutional layer with 64 5x5 filters and ReLU activation function, followed by

[0150] iii. Generate 2x2 max pooling of 64x64x4 output, followed by

[0151] iv. A convolutional layer with 128 5x5 filters and ReLU activation function, followed by

[0152] v. Generate 2x2 max pooling of 32x32x128 output, followed by

[0153] vi. A convolutional layer with 256 5x5 filters and ReLU activation function, followed by

[0154] vii. Generate 2x2 max pooling of 16x16x256 output, followed by

[0155] viii. A convolutional layer with 512 5x5 filters and ReLU activation function, followed by

[0156] ix. Generate 2x2 max pooling of 8x8x512 output, followed by

[0157] x. A convolutional layer with 512 5x5 filters and ReLU activation function, followed by

[0158] xi. Generate 2x2 max pooling of 4x4x512 output, followed by

[0159] xii. Generate K scalar-valued fully connected layers, where K is the number of classes that the cell images are classified into. For example, if the NN is trained to classify cell images into one of the positive and non-positive classes, then K is equal to 2. For example, if the NN is trained to classify cell images into one of the graphic classes, then K is equal to 5.

[0160] These classifications can then be compared to thresholds (e.g., expected percentages or numbers of specific particle types), and if one or more thresholds are exceeded (or, in the case of a lower threshold not being met), a system implemented based on the present disclosure can determine 603 that a corresponding verification indication (e.g., a flag) should be presented to the user. For example, if an abnormal cell type exceeds a certain percentage (e.g., if RBC fragments exceed a 2.5% threshold), it is marked as abnormal—or, for example, if an abnormal cell type exceeds a certain count in a blood sample (e.g., more than three blastocysts), it is marked as abnormal. These counts or specific percentages can be based on customized programming rules, rules set by the user, or rules derived from actual laboratory standards. These verification indications can also be provided along with a description of the abnormal particle type that triggered the indication in the case of an abnormal particle type. These verification indications are particularly helpful in pointing out abnormal particle types to the user, allowing them to verify any associated abnormal particle images on the screen without the need for subsequent confirmation testing (e.g., smears) and helping to confirm the abnormal particle type.

[0161] When classifying cells, there may be several types of scores associated with the cells. For example, a cell will have to exceed a certain classification threshold to be labeled as a first cell type (e.g., platelet), then exceed an additional classification threshold to be labeled as an abnormal cell type (e.g., giant platelet), and finally will need to exceed a specific numerical threshold to invoke a check indication associated with an abnormal cell type (e.g., a flag for giant platelets). For example, an imaged cell may need to have a confidence score of over 60% to be designated as a platelet, a confidence score of 50% to be designated as a giant platelet (or alternatively, once designated as a platelet, it is sent to a subclassifier, and the subclassification will need to exceed a specific threshold - e.g., 70% to be designated as giant platelets), and then the total number of giant platelets will need to exceed a numerical threshold (e.g., 2.5%) to label the sample as giant platelets. Note that these are illustrative examples, and any range of confidence scores and numerical thresholds may be used, and it is likely that different confidence scores and different numerical thresholds may be used for different cell types.

[0162] Furthermore, the verification indication of abnormal cell types can be different from the verification of abnormal cell types in images. For example, all giant platelets can be treated as a separate class of images unique to those cell types (e.g., images with a giant platelet cell class associated with giant platelets). However, in order to trigger a verification indication (flagging a sample as having an abnormally high number of giant platelets), a specific threshold score for that indication (e.g., 2.5%) would need to be exceeded.

[0163] Examples of such abnormal cell types and corresponding descriptions are provided in Table 3 below.

[0164]

[0165] Table 3

[0166] Which verification indications can be determined and how they are determined can be based on characteristics of the 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 PLT and platelet identification is based on impedance specified by PLT-i. In this case, the platelet result generated using the imaging technology may be the primary parameter for reporting purposes (e.g., displayed on a results screen along with other parameters, while the PLT-i result may be available only via a lower level screen), and the results of both the PLT and PLI-i can be used to determine whether to provide a notification and accompanying description to the user based on, for example, the logic listed in Table 4 below.

[0167]

[0168] Table 4

[0169] In addition to or as an alternative to the approach described in the context of Table 4, another example of an approach that can be taken is to determine a flag based on the confidence or test result value. For example, in some cases, the analyzer can be configured with a built-in confidence threshold, and results generated with a confidence level below the threshold can be accompanied by a confidence flag indicating that the result is of low confidence and may require additional verification. As another example, in some cases, the user of the analyzer can be allowed to define various range limits, such as a reference limit, an action limit, and a critical limit. In this case, when a result is outside a specified limit range, a flag can be provided indicating the limit it exceeded.

[0170] In any case, once the results are determined, an interface can be displayed 604 that can include various parameters and / or verification instructions derived from the image, impedance, or other data associated with the sample and corresponding descriptions. Examples of such interfaces are shown in FIG. Figure 7 In the interface shown in this figure, a work list 701 is presented to the user, which work list 701 includes a set of check instructions 702 and descriptions 703 of those check instructions. Figure 7 The interface also provides the user with a classification for different verification indications (i.e., "Sample Quality" and "Morphology Message") and a brief description of the type of verification and / or other remedial measures that may be appropriate based on the displayed verification indication. To assist in this verification, Figure 7 The interface displays 605 a collection of thumbnail cell images 704 corresponding to images to be verified based on a verification indicator. For example, if the description of the verification indicator indicates that platelet clumps were detected in the sample, a set of thumbnail cell images can be presented, showing thumbnails of images in which platelet clumps were detected. These images can be presented in order based on their contribution to the corresponding verification indication (e.g., platelet clump images can be sorted by the size of the depicted clumps or the confidence level in identifying the clumps), and when a thumbnail image is clicked or otherwise selected, a full-resolution copy of the image corresponding to the selected thumbnail can be displayed so that the user can perform the appropriate verification task.

[0171] There are also many variations in the presentation of the verification prompts and thumbnail cell images in the above examples. For example, in some cases, instead of displaying a collection of thumbnail cell images corresponding to the items in the worklist, a parameter list and a corresponding verification notification can be provided to the user, and in response to selecting the notification (or its corresponding parameter), a collection of thumbnail cell images specifically for that parameter can be provided to the user. As another example of potential changes that may exist in some implementations, there are different methods for presenting thumbnail cell images. For example, such thumbnail cell images can be presented in an order sorted by factors such as capture order, size, shape, standard deviation from the mean, etc. It is also possible that, in some cases, a verification instruction can be provided that is not associated with a specific image. For example, if a non-imaging modality (e.g., impedance) identifies a specific unexpected cell type in a sample, a verification instruction can be provided, wherein the information indicates that a reflex test should be performed for the unexpected cell type, but may not be accompanied by (or associated with) the thumbnail cell images described above.

[0172] In addition to the presentation of verification indications and thumbnail cell images, other types of variations are possible. To illustrate, consider a potential verification indication that can 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 can be a low confidence flag that some implementations can provide when 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 verification indication can be displayed (e.g., a flag that has a different appearance than a flag that can be displayed for platelet clumps or a different type of symbol entirely), and the user can be allowed to view a thumbnail of the cell image corresponding to the low confidence verification indicator (e.g., an image of a red blood cell that is identified as having a confidence below the threshold). As another example, in the event that counts exceed a built-in threshold corresponding to a level at which the analyzer claims to be accurate (e.g., the analyzer claims to be able to accurately count up to X cell types, and detects X+Y counts of that cell type), a linearity check indication may be provided along with thumbnail cell images of the cell types whose counts exceeded the threshold and a message indicating that the sample should be rerun after dilution.

[0173] As the example of another type of variation, in some cases, the user can be able to specify one or more threshold values, and the one or more threshold values should be applied to various counting to trigger an inspection indication. For example, the user can define a first group of high thresholds and low thresholds for a cell type, and define a second group of high thresholds and low thresholds for the cell type. In this case, if the counting for the cell type is outside the first group of high thresholds and low thresholds but not outside the second group of high thresholds and low thresholds, an inspection indication (for example, painted as a yellow mark) with a first feature can be provided, and if the counting for the cell class is outside the second group of high thresholds and low thresholds, an inspection indication (for example, painted as a red mark) with a second feature can be provided. Therefore, the above-provided inspection indication and the example of potential triggering thereof should only be understood to be illustrative, and should not be considered as limiting the scope of protection provided by this document or any other document claiming to benefit from this document.

[0174] Multi-channel system

[0175] As discussed herein, a sample can be segmented (e.g., divided into aliquots) to allow for various types of testing. Thus, in some embodiments, a sample analysis system can include an aliquoter configured to divide a sample into aliquots, wherein a controller (e.g., a processor) is programmed to cause a fluid system to control the flow of the aliquots based on a parameter whose value is to be determined.

[0176] Now refer to Figure 8 , shows an illustrative flow chart for a dual-channel system. As will be described in more detail below, a dual-channel system can be configured to capture high-quality images of microscopic particles (e.g., blood cells) in a first aliquot of a sample fluid in a flow cell via an imaging system according to the above description and to analyze a second aliquot of the same sample fluid via a suitable alternative system according to the description herein. In some embodiments and as shown, the system can capture 801 an image of blood cells (e.g., an aliquot) in a flow cell and measure 802 the impedance of the blood cells passing through the alternative system. Thus, in the current illustrative example, the dual-channel system includes an imaging system according to the above description and an impedance system according to the above description. Note that other embodiments can 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, the term can also be used synonymously with module and is intended to indicate the use of different analytical processes to analyze particles—in this concept, each channel or module uses a different analytical technique for particle analysis (e.g., an imaging technique different from an impedance technique, which in turn is different from a spectrophotometric technique).

[0177] Although Figure 8The illustrative example shown in describes measuring 802 the impedance of blood cells passing through an alternative system, but it should be understood that alternative systems that do not measure impedance, such as the fluorescence image analysis device 2001 and / or the spectrophotometer system 3000 described above, can be used to analyze blood cells in the sample fluid. It should also be understood that while this illustrative example is described in terms of channels of an imaging system and channels of an alternative system, various implementations may include any number of channels using any type of different measurement systems (e.g., fluorescence systems, light scattering systems, and / or spectrophotometric systems). Thus, it should be understood that a multi-channel system (including but not limited to a dual-channel system) can utilize an imaging system having a flow cell 22, a high optical resolution imaging device 24, and a processor 18 to capture images from a first aliquot of the sample fluid, and that the other channels of the multi-channel system can include any other suitable system configured to appropriately analyze other aliquots of the sample fluid.

[0178] Once the image 801 is captured and the impedance 802 is measured, the system can utilize the analysis module to determine values for a first plurality of parameters using data from the flow imaging module 803 and use data from the alternative system (or any other suitable alternative system as will be apparent to those skilled in the art in view of the teachings herein) to determine values for a second plurality of parameters 804. As an example, the system can determine 803 one or more image-based numerical values based on analysis of the captured 801 image of blood cells and determine 804 one or more numerical parameters based on measurements 802 from the alternative system (e.g., an impedance system).

[0179] The first and second parameters can then be analyzed 805 to identify a confidence score or verification indication. The first and second parameters can be analyzed 805 for any other suitable purpose, as will be apparent to those skilled in the art in view of the teachings herein. In addition, the system can present the determined values 803, 804 (which can include one or more image-based numerical values and one or more numerical parameters based on the measurement 802 of the alternative system) to the user via a computing interface.

[0180] In some cases, at least one of the first measurement parameters from the imaging system described above and at least one of the second measurement parameters from a suitable alternative system of the multi-channel system (e.g., a dual-channel system or two channels within a more than two-channel arrangement) are similar and / or identical. Similar and / or matching measurement parameters from the imaging system and the alternative system to the multi-channel system can be used by the multi-channel system for any suitable purpose, as will be apparent to those skilled in the art in view of the teachings herein.

[0181] In another embodiment, the first parameter (e.g., a parameter associated with the captured image) may include, but is not limited to, the percentage of nucleated red blood cells, the number of nucleated red blood cells, the percentage of unclassified white blood cells, the number of unclassified white blood cells, the percentage of neutrophils, the number of neutrophils, the percentage of immature granulocytes, the number of immature granulocytes, the percentage of lymphocytes, the number of lymphocytes, the percentage of monocytes, the number of monocytes, the percentage of eosinophils, the number of eosinophils, the percentage of basophils, the number of basophils, the percentage of reticulocytes, the number of reticulocytes, and the 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, the mean cell volume, the mean corpuscular hemoglobin, the mean corpuscular hemoglobin concentration, the red cell distribution width, the standard deviation of the red cell distribution width, and the mean platelet volume.

[0182] Unclassified cells are cells that do not exceed a specific classification threshold for being designated as a cell type. In various examples, unclassified cells can be placed in a general unclassified classification bucket in which the image is displayed for user review (e.g., manually marking / classifying these cells on the screen). Cells marked as unclassified white blood cells can be classified as white blood cells (e.g., exceeding a first confidence threshold for being classified as a white blood cell), but do not meet a confidence threshold for being classified as a specific type of white blood cell (e.g., one of a 5 or 6 partial WBC differential).

[0183] Processing Architecture

[0184] Next go to Figure 9 , this figure is a simplified block diagram of an exemplary module system that can be used to perform various logics described herein and / or control various parts.Module system 900 can be a part for a cell analysis system or be connected to a cell analysis system.Module system 900 is very suitable for generating data or receiving the input relevant to analysis. In some cases, module system 900 includes a hardware component electrically coupled via a bus subsystem 902, and the hardware component includes 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, system 900 includes a network interface 910 and / or a diagnostic system interface 940, which can receive signals from a diagnostic system 942 and / or send signals to a diagnostic system 942. In some cases, system 900 includes, for example, software components, operating systems 916 and / or other codes 918 currently located in the working memory 912 of a memory 914, and the other codes 918 are, for example, programs configured to realize one or more aspects of the technology disclosed herein.

[0185] In some embodiments, the 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 various aspects of the methods described herein can be stored in the storage subsystem 920. These software modules can be executed by one or more processors 904. In a distributed environment, the software modules can be stored on multiple computer systems and executed by the processors of multiple computer systems. The storage subsystem 920 can include a memory subsystem 922 and a file storage subsystem 928. The memory subsystem 922 can 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. The file storage subsystem 928 can provide persistent (non-volatile) storage for program and data files and can include tangible storage media that can optionally embody patient, treatment, assessment, or other data. The file storage subsystem 928 may include a hard drive, a floppy disk drive and associated removable media, a compact digital read-only memory (CD-ROM) drive, an optical drive, a DVD, a CD-R, a CD RW, a solid-state removable memory, other removable media cartridges or disks, and the like. One or more of the drives may be located at a remote location on other connected computers coupled to the module system 900 at other sites. In some cases, the system may include a computer-readable storage medium or other tangible storage medium storing one or more instruction sequences or codes that, when executed by one or more processors, may enable one or more processors to perform any aspect of the technology or method disclosed herein. One or more modules that implement the functionality of the technology disclosed herein may be stored by the file storage subsystem 928. In some embodiments, the software or code will provide a protocol to enable the module system 900 to communicate with the communication network 930. Alternatively, such communication may include dial-up or Internet connection communication.

[0186] Should be understood that system 900 can be configured to perform or make system perform various aspects of method as described herein.For example, processor component 904 can be a microprocessor control module, and this microprocessor control module is configured to receive cell parameter signal 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 communication network 930 alternatively.Processor component 904 can also be configured to send cell parameter signal according to any technology process disclosed herein alternatively to sensor output device or module 936, user interface output device 908, network interface device 910, diagnostic system interface 940 or its any combination.Each in device or module described herein can comprise one or more software modules or hardware modules or its any combination on the computer readable medium processed by processor.

[0187] User interface input device 906 can comprise for example touch pad, keyboard, pointing device such as mouse, trackball, graphic input board, scanner, joystick, the touch screen that is incorporated into the display, the audio input device such as speech recognition system, microphone and other types of input device.User input device 906 can also download computer executable code from tangible storage medium or from communication network 930, and this code embodies any method disclosed herein or its aspect.Should be understood that terminal software can be updated from time to time as appropriate and be downloaded to terminal.Usually, the use of term " input device " is intended to comprise multiple conventional and proprietary device and mode that information is input into module system 900.

[0188] The user interface output device 906 can 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 can also provide non-visual display such as via an audio output device. Usually, the 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 the user. The bus subsystem 902 provides a mechanism for making the various components and subsystems of the module system 900 communicate with each other according to expectation or expectation. The various subsystems and components of the module system 900 do not need to be in the same physical location, but can be distributed in different locations within the distributed network. Although the bus subsystem 902 is schematically shown as a single bus, the alternative embodiment of the bus subsystem can utilize multiple buses.

[0189] The network interface 910 can provide an interface to an external network 930 or other device. The external communication network 930 can be configured to communicate with other parties as needed or desired. Therefore, the communication network 930 can receive electronic data packets from the module system 900 and send any information back to the module system 900 as needed or desired. As depicted here, the communication network 930 and / or the diagnostic system interface 942 can send information to the diagnostic system 942 or receive information from the diagnostic system 942. In addition to providing such an infrastructure communication link within the system, the communication network system 930 can also provide connections to other networks such as the Internet and can include wired, wireless, modem and / or other types of interface connections. The network interface 910 can also allow a module system to interface with one or more other systems to jointly provide the functionality of the functions described herein. For example, in some cases, a first module system local to the analyzer can 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) can receive the data from the first module system and analyze it to provide results such as can be provided on the user interface output device 908 of the first module system.

[0190] It will be apparent to those skilled in the art that substantial variations may be used depending on specific requirements. For example, customized hardware may also be used, and / or specific elements may be implemented in hardware, software (including portable software, such as applets), or both. In addition, connections to other computing devices, such as network input / output devices, may be employed. The modular terminal system 900 itself may be of various types, including computer terminals, personal computers, portable computers, workstations, network computers, or any other data processing system. Due to the ever-changing nature of computers and networks, Figure 9 The description of the module system 900 depicted in FIG is intended only as a specific example for purposes of illustration. Many other configurations of the module system 900 may have more Figure 9 The depicted modular system may include more or fewer components. Any module or component of modular system 900, or any combination of such modules or components, may be coupled to, integrated into, or otherwise configured to interface with any cell analysis system embodiment disclosed herein. Relatedly, any of the hardware and software components discussed above may be integrated with or configured to interface with other medical assessment or treatment systems used elsewhere.

[0191] Example of sample preparation process

[0192] In the system described in this article, Figure 11The process shown can be used to perform sample preparation prior to analyzing a sample fluid as described herein. Figure 8 In the process, at step 2601, a stain can be delivered to a chamber, such as a mixing chamber, RBC chamber 4015 and / or WBC chambers 4020, 4025 as described herein. This can include, for example, delivering the stain to the chamber via a stain dispenser. Then, at step 2602, the stain can be preheated within the chamber, such as via induction heating. Next, at step 2603, a sample can be delivered to the chamber. This can include, for example, delivering the sample to the chamber via a sample dispenser (e.g., probe 4005) so that it is added to the stain. In some embodiments, delivering the sample to the chamber can include mixing the sample with a preheated stain within the chamber. Figure 11 During the process, then, at step 2604, a homogeneous sample mixture can be formed in the chamber. This can include, for example, using fluid energy to mix the sample with the stain, such as by cyclically pulling the sample out of the chamber and pushing the sample back into the chamber via corresponding tangential ports of the housing to perform countercurrent mixing. Alternatively, this can include using a magnet to drive a spherical ferromagnetic ball placed in the chamber to perform stirring mixing. As another example, this can include introducing one or more bubbles at the bottom of the chamber to generate a vortex.

[0193] The homogenized sample mixture can then be heated within the chamber, for example, via induction heating or resistive heating, at step 605. In some embodiments, the homogenized sample mixture can be heated to a threshold temperature via induction heating or resistive heating and then maintained at the threshold temperature via a maintenance heater.

[0194] After the homogenized sample mixture reaches a threshold temperature, the sample mixture can be transferred to a flow cell such as Figure 1 The flow cell 22 is used for imaging by an imaging device such as Figure 1 The high optical resolution imaging device 24 is used for imaging.

[0195] While 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 a tube having a lumen (not shown) in 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, entitled “Systems and Methods for Imaging Fluid Samples,” the disclosure of which is hereby incorporated by reference in its entirety.

[0196] In some embodiments, the addition of the diluent is part of the preparation step, wherein the diluent is added to each chamber before, after, or both before and after the addition of the blood sample to each chamber. For example, the RBC chamber may receive the diluent as the primary or sole preparation reagent, while the WBC chamber may receive both the diluent and the stain.

[0197] It should be understood that the preparation steps for the RBC chamber can be different from those for the WBC chamber. For example, the RBC chamber may utilize a preparation step that involves: a) receiving a diluent, then a blood sample; b) receiving a blood sample, then a diluent; or c) receiving a diluent, then a blood sample, then additional diluent; but not receiving a stain. In this way, the preparation time for the RBC chamber can be shorter, and the workflow can involve running an RBC sample through an imaging process while a WBC sample is still being prepared.

[0198] In some embodiments, the staining reagent utilizes both a lysing agent (for lysing red blood cells) and a staining agent (for penetrating the remaining white blood cells, staining the interior area, and fixing the white blood cells so that the staining agent does not escape). In this way, a single staining reagent can be used to treat certain types of cells (e.g., white blood cells) to both eliminate red blood cells and stain the remaining white blood cells. Other embodiments can utilize multiple compositions, such as a first lysing reagent for lysing red blood cells and a second staining reagent for staining white blood cells, wherein the workflow will involve a chamber (e.g., a WBC chamber) receiving a separate lysing reagent and a separate staining reagent to prepare a WBC sample for visualization.

[0199] In some embodiments, various chambers (for example, RBC chamber 4015 and WBC chamber 4020, 4025) are not meant to be strictly used for preparing dedicated cell types, or in other words, cell types can be rotated. For example, the chamber can be alternately used for RBC preparation and WBC preparation. In this way, once the sample in the chamber is ready for imaging, just can utilize cleaning cycle to clean the chamber before receiving subsequent blood samples (for example, chamber can first be configured to prepare WBC and reach a certain amount of identical preparation operation, then prepare RBC and reach a certain amount of sample preparation operation, for example, 1 WBC preparation, then 1 RBC preparation, or 2 WBC preparations, then 1 RBC preparation, then 2 or more WBC preparations etc.). Between sample operations, can use cleaning agents such as diluents or detergents to eliminate carryover. Even when specific chamber is only used for specific cell types (for example, 4020 is only used as the WBC chamber), also can run cleaning steps after sample is prepared and imaging, to eliminate residual.

[0200] Other embodiments may still 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 can be uniquely adapted for various workflows. For example, a first chamber can be used to prepare a white blood cell sample, including receiving at least a WBC stain and a lysis reagent, while a second chamber can be used to prepare a platelet sample, receiving at least a platelet reagent different from the WBC stain and lysis reagents.

[0201] Please note that although the terms white blood cell (WBC) chamber and red blood cell (RBC) chamber are used to represent sample preparation chambers for imaging, the sample imaged as a result of the preparation process can allow for biological imaging of multiple cell types. For example, the WBC chamber utilizes a lysis agent to eliminate red blood cells, however, the lysis agent can still retain platelets and reticulocytes, so the sample prepared in the WBC chamber can still at least image, for example, white blood cells, platelets, and reticulocytes. Similarly, the RBC chamber can receive a preparation procedure different from the WBC chamber (e.g., no lysis or no staining / lysis combination reagent), but the sample prepared in the RBC chamber can still enable multiple cell types such as red blood cells - and one or more of white blood cells, platelets, and reticulocytes to be visualized. Additional information about how samples can be prepared for analysis in some embodiments and, in particular, how stains can be applied in some cases is provided in U.S. patent application Ser. No. 18 / 224,947, the disclosure of which is incorporated herein by reference in its entirety.

[0202] Additional Examples

[0203] To further illustrate potential implementations and embodiments of the disclosed technology, exemplary systems and methods that can be practiced based on the present disclosure are set forth below.

[0204] Example 1A

[0205] A sample analysis system comprises: a) a flow cell; b) a fluid system for flowing 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 programmed to perform the following actions, the actions comprising: i) analyzing the multiple images to determine whether a checkpoint indication applies to the multiple images; ii) displaying an interface having a checkpoint indication and a description of the checkpoint indication; and iii) displaying an interface having at least one cell image corresponding to the checkpoint indication.

[0206] Example 2A

[0207] The sample analysis system according to Example 1A, wherein the one or more processors are configured to determine that a user-defined check condition is satisfied; and in response to determining that the user-defined check condition is satisfied, display an interface with a check indication.

[0208] Example 3A

[0209] The sample analysis system according to Example 1A, wherein the check indication is at least one of a high count indication or a low count indication.

[0210] Example 4A

[0211] The sample analysis system according to Example 1A, wherein the one or more processors are configured to determine that a check-in indication should be displayed based on a built-in check-in condition being satisfied.

[0212] Example 5A

[0213] The sample analysis system according to Example 1A, wherein the one or more processors are configured to determine that the audit indication should be displayed based on at least one of: a low confidence condition being satisfied and a linearity condition being not satisfied.

[0214] Example 6A

[0215] The sample analysis system according to Example 1A, wherein the one or more processors are programmed to determine that a check indication should be displayed based on detecting at least one of platelet clumps or red blood cell clumps in the plurality of images of the blood cells.

[0216] Example 7A

[0217] The sample analysis system of Example 1A, wherein the one or more processors are programmed to determine that a check indication should be displayed based on detecting at least one of the following in the plurality of images of blood cells: red blood cell fragments, sickle cells, type II cells, large platelets, giant platelets, reticulocytes, mutant lymphocytes, or blasts.

[0218] Example 8A

[0219] The sample analysis system according to Example 1A, wherein the interface includes a plurality of checkpoint indications, and wherein the interface displays a description of each checkpoint indication and at least one cell image corresponding to each checkpoint indication.

[0220] Example 9A

[0221] The sample analysis system according to Example 1A further includes a non-transitory computer-readable medium having stored thereon a machine learning algorithm, the machine learning algorithm being trained to analyze images from a plurality of images to classify particles depicted in the images, wherein the one or more processors are programmed to determine that the verification indication applies to the plurality of images based on confidence scores provided by the machine learning algorithm for the classification of particles depicted in the plurality of images.

[0222] Example 10A

[0223] The sample analysis system according to Example 1A further includes a non-transitory computer-readable medium that stores multiple conditions for determining whether a corresponding verification indication should be provided, wherein the multiple conditions include 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 the user of the sample analysis system.

[0224] Example 11A

[0225] A sample analysis system according to Example 10A, wherein: a) each user-defined condition in 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 the particular cell type and a second set of high and low thresholds for the particular cell type; c) one or more processors are programmed to: i) determine that a first check indication applies to multiple images when the count of the particular cell type is outside the first set of high and low thresholds and included in the second set of high and low thresholds; and ii) determine that a second check indication applies to multiple images when the count of the particular cell type is outside the second set of high and low thresholds; and d) the first check indication and the second check indication are visually distinguishable from each other.

[0226] Example 12A

[0227] The sample analysis system according to Example 11A, wherein the first check indicator and the second check indicator have different colors.

[0228] Example 13A

[0229] A sample analysis system according to Example 1A, wherein at least one cell image corresponding to the verification indication includes a thumbnail cell image, and wherein the one or more processors are programmed to display a full-resolution image of the blood cells corresponding to the thumbnail cell image captured by the image capture device in response to receiving a signal indicating that a user has selected the thumbnail cell image.

[0230] Example 14A

[0231] The sample analysis system of Example 1A, wherein the at least one cell image corresponding to the verification indication includes a plurality of thumbnail cell images corresponding to the verification indication, and wherein the plurality of thumbnail cell images corresponding to the verification indication are sorted based on their respective contributions to the verification indication.

[0232] Example 15A

[0233] A sample analysis system according to Example 1A, wherein a) the one or more processors include: i) a first processor programmed to analyze multiple images to determine whether a verification indication applies to the multiple images; and ii) a second processor programmed to display an interface; b) the second processor constitutes an analyzer, which also includes a flow cell and a fluid system; and c) the first processor does not constitute an analyzer and is separated from the second processor by a wide area network and communicates with the second processor via the wide area network.

[0234] Example 16A

[0235] A method for analyzing a sample includes: a) using a fluid 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 the blood cells pass through the flow cell; c) using one or more processors to perform a set of actions, the set of actions including: i) analyzing the multiple images to determine whether a verification indication applies to the multiple images; ii) displaying an interface having a verification indication and a description of the verification indication; and iii) displaying an interface having at least one cell image corresponding to the verification indication.

[0236] Example 17A

[0237] The sample analysis method according to Example 16A, wherein the method includes determining that a user-defined check condition is satisfied; and wherein displaying the interface is executed in response to determining that the user-defined check condition is satisfied.

[0238] Example 18A

[0239] The sample analysis method according to Example 16A, wherein the check indication is at least one of a high count indication or a low count indication.

[0240] Example 19A

[0241] The sample analysis method according to Example 16A, wherein analyzing the plurality of images to determine whether a check indication applies to the plurality of images comprises determining that the check indication should be displayed based on a built-in check condition being satisfied.

[0242] Example 20A

[0243] The sample analysis method according to Example 16A, wherein analyzing the plurality of images to determine whether the check indication applies to the plurality of images comprises determining that the check indication should be displayed based on at least one of: satisfying a low confidence condition and failing to satisfy a linearity condition.

[0244] Example 21A

[0245] The sample analysis method according to Example 16A, wherein analyzing the plurality of images to determine whether a check indication applies to the plurality of images includes determining that the check indication 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.

[0246] Example 22A

[0247] The sample analysis method of Example 16A, wherein analyzing multiple images to determine whether a check indication is applicable to the multiple images includes determining that a check indication should be displayed based on detecting at least one of the following in the multiple images of blood cells: red blood cell fragments, sickle cells, type II cells, large platelets, giant platelets, reticulocytes, mutant lymphocytes, or blasts.

[0248] Example 23A

[0249] The sample analysis method according to Example 16A, wherein the interface includes a plurality of verification indications, and wherein the interface displays a description of each verification indication and at least one cell image corresponding to each verification indication.

[0250] Example 24A

[0251] A sample analysis method according to Example 16A, wherein analyzing multiple images to determine whether a verification indication applies to the multiple images includes: a) using a machine learning algorithm that is trained to analyze images from the multiple images to classify particles depicted in those images; and b) determining that the verification indication applies to the multiple images based on a confidence score provided by the machine learning algorithm for the classification of particles depicted in the multiple images.

[0252] Example 25A

[0253] A sample analysis method according to Example 16A, wherein analyzing multiple images to determine whether verification instructions apply to the multiple images includes: retrieving multiple conditions from a non-transitory computer-readable medium for determining whether corresponding verification instructions should be provided, wherein the multiple conditions include 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 the user of the sample analysis system.

[0254] Example 26A

[0255] A sample analysis method according to Example 25A, wherein: a) each user definition in 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 a first check indication is applicable to multiple images based on whether the count of the specific cell type is outside the first set of high and low thresholds and is contained within the second set of high and low thresholds; and ii) determining whether a second check indication is applicable to multiple images based on whether the count of the specific cell type is outside the second set of high and low thresholds; and d) the first check indication and the second check indication are visually distinguishable from each other.

[0256] Example 27A

[0257] The sample analysis method according to Example 26A, wherein the first check indicator and the second check indicator have different colors.

[0258] Example 28A

[0259] A sample analysis method according to Example 16A, wherein: a) at least one cell image corresponding to the verification indication includes a thumbnail cell image; and b) the method includes: i) receiving a signal indicating that a user has selected the thumbnail cell image; and ii) in response to receiving the signal indicating that the user has selected the thumbnail cell image, displaying a full-resolution image of the blood cells corresponding to the thumbnail cell image captured by the image capture device.

[0260] Example 29A

[0261] A sample analysis method according to Example 16A, wherein at least one cell image corresponding to a verification indication includes multiple thumbnail cell images corresponding to the verification indication, and wherein the method includes: sorting the multiple thumbnail cell images corresponding to the verification indication based on their respective contributions to the verification indication.

[0262] Example 30A

[0263] A sample analysis method according to Example 16A, wherein: a) the one or more processors include: i) a first processor programmed to analyze multiple images to determine whether a verification indication applies to the multiple images; and ii) a second processor programmed to display an interface; b) the second processor constitutes an analyzer, which also includes a flow cell and a fluid system; and c) the first processor does not constitute an analyzer and is separated from the second processor by a wide area network and communicates with the second processor via the wide area network.

[0264] Example 31A

[0265] A method of using a bioanalyzer comprises: a) flowing a portion of a sample through a flow cell using a fluidics system; b) capturing a plurality of images of blood cells as they pass through the flow cell using an image capture device; and c) viewing a checksum indication associated with the sample; and d) checking the checksum indication by accessing data corresponding to the checksum indication through a user interface.

[0266] Example 32A

[0267] The method of Example 31A, wherein: a) a check indication associated with the sample is associated with at least a portion of the plurality of images; and b) checking the check indication is performed by accessing data corresponding to the check indication through a user interface by checking at least a subset of at least the portion.

[0268] Example 33A

[0269] A method according to Example 32A, wherein: a) the method includes: i) viewing a collection of thumbnail images of cells of a type corresponding to the verification indication; and ii) selecting a thumbnail from the collection of thumbnail images; and b) verifying a subset of at least a portion of the plurality of images includes: viewing the full resolution image corresponding to the selected thumbnail.

[0270] Example 34A

[0271] The method of Example 33A, wherein the method includes selecting sorting criteria for the set of thumbnails of cell images having a type corresponding to the audit indication.

[0272] Example 35A

[0273] The method of Example 32A, wherein: a) accessing data corresponding to a verification indication includes verifying a message indicating an abnormal measurement derived from multiple images of blood cells; and b) the method includes confirming whether the abnormal measurement derived from the multiple images is correct based on verifying additional information corresponding to the abnormal result.

[0274] Example 36A

[0275] The method of Example 35A, wherein confirming whether the abnormal measurement derived from the plurality of images is correct based on verifying additional information corresponding to the abnormal result comprises: viewing one or more full-resolution images from the plurality of images of blood cells.

[0276] Example 37A

[0277] The method of Example 35A, wherein confirming whether the abnormal measurement derived from the plurality of images is correct based on verifying additional information corresponding to the abnormal result comprises: reviewing results derived by a non-imaging measurement system.

[0278] Example 38A

[0279] The method of Example 37A, wherein the abnormality measurement derived from the plurality of images is a count of cells of a type, and wherein the result derived by the non-imaging measurement system is a count of cells of the same type.

[0280] Example 39A

[0281] The method of Example 38A, wherein the method comprises determining whether to run a count for the same type of cells using a new portion of the sample based on confirming whether the abnormal measurement derived from the plurality of images is correct.

[0282] Example 40A

[0283] The method according to Example 31A, wherein the method further comprises: defining a verification condition for at least one cell type among the plurality of cell types.

[0284] Example 41A

[0285] The method according to Example 40A, wherein the check condition comprises a plurality of sets of thresholds, wherein each set of thresholds comprises a high threshold and a low threshold.

[0286] Example 42A

[0287] The method of Example 31A, wherein the method comprises determining that additional analysis should be performed on the sample based on accessing data corresponding to the audit indication through the user interface.

[0288] Example 43A

[0289] The method of Example 42A, wherein: a) the additional analysis includes capturing images of reticulocytes in the sample; b) the method includes a user accessing one or more of the images of reticulocytes; and c) accessing data corresponding to the verification indication through the user interface includes accessing a reticulocyte count for the sample.

[0290] Example 44A

[0291] The method of example 42A, wherein: a) accessing data corresponding to the verification indication comprises: verifying a message indicating a sample count based on a portion of the sample exceeding a maximum approved count; and b) the additional analysis comprises redetermining the count using a new portion of the sample.

[0292] Example 45A

[0293] The method of example 44A, wherein the method comprises diluting the new portion of the sample to a higher dilution level than a dilution level of a portion of the sample used to form a basis for the count exceeding the maximum approved count.

[0294] Example 1B

[0295] A sample analysis system comprises: a) a fluidic system for: i) flowing 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, the image capture device being configured to capture multiple images of cells of the first portion of the blood sample; and ii) flowing 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 of the second portion of the blood sample; and b) one or more processors programmed to: i) determine one or more numerical parameters of the cells of the second portion of the blood sample; and ii) present a computing interface comprising the multiple images of the cells of the first portion of the blood sample and the one or more numerical parameters of the cells of the second portion of the blood sample.

[0296] Example 2B

[0297] A sample analysis system according to Example 1B, wherein the sample analysis system further comprises an aliquoter configured to separate the blood sample into a plurality of aliquots, wherein the first portion is a first aliquot of the plurality of aliquots and the second portion is a second aliquot of the plurality of aliquots.

[0298] Example 3B

[0299] The sample analysis system according to Example 1B is configured to: a) receive a blood sample in a container with a barcode; b) read the barcode; and c) determine one or more tests for the blood sample based on the barcode.

[0300] Example 4B

[0301] A sample analysis system according to Example 1B, wherein: a) the fluid system is used to flow a first subportion of a first portion of the blood sample through a flow imaging module for red blood cell (RBC) imaging in a first flow pool; and b) the fluid system is used to flow a second subportion of the first portion of the blood sample through the flow imaging module for white blood cell (WBC) imaging, the second subportion being treated with a stain composition.

[0302] Example 5B

[0303] The sample analysis system according to Example 1B, wherein the second module includes an impedance analyzer.

[0304] Example 6B

[0305] The sample analysis system according to Example 1B, wherein the second module includes a fluorescence analyzer.

[0306] Example 7B

[0307] The sample analysis system according to Example 1B, wherein the numerical parameter is selected from the group consisting of mean corpuscular volume, cell count, and hemoglobin concentration.

[0308] Example 8B

[0309] The sample analysis system according to Example 1B, wherein the plurality of images includes images of the first cell type and images of the second cell type.

[0310] Example 9B

[0311] The sample analysis system according to Example 1B, wherein the plurality of cells includes a first cell type, and wherein 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.

[0312] Example 10B

[0313] The sample analysis system of Example 1B, wherein the plurality of cells includes a first cell type and a second cell type, and wherein the computing interface is configured to allow a user to select the first cell type and the second cell type and in response display an image of the first cell type and an image of the second cell type.

[0314] Example 11B

[0315] The sample analysis system according to 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 a computing interface.

[0316] Example 12B

[0317] The sample analysis system according to Example 1B, wherein the second module is further configured to test one or more numerical parameters of the first cell type and to test one or more numerical parameters of the second type.

[0318] Example 13B

[0319] The sample analysis system according to Example 1B, wherein the second module is configured to determine more than one parameter of the first cell type.

[0320] Example 14B

[0321] The sample analysis system according to Example 1B, wherein the computing interface is configured to provide a plurality of images of cells of a first portion of the blood sample and one or more numerical parameters of a second portion of the blood sample on a single screen.

[0322] Example 15B

[0323] A sample analysis system according to Example 1B, wherein: a) the one or more processors include: i) a first processor programmed to determine one or more parameters of cells of a second portion of the blood sample; and ii) a second processor programmed to present a computing interface comprising a plurality of images of the cells of the first portion of the blood sample and one or more numerical parameters of the cells of the second portion of the blood sample; b) the second processor constitutes an analyzer that also includes a fluid system; and c) the first processor does not constitute an analyzer and is separate from and communicates with the second processor via a wide area network.

[0324] Example 16B

[0325] A method for analyzing a sample comprises: a) using a fluidic system: i) flowing 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, the image capture device being configured to capture multiple images of cells of the first portion of the blood sample; and ii) flowing 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 of the second portion of the blood sample; and b) using one or more processors: i) determining one or more numerical parameters of the cells of the second portion of the blood sample; and ii) presenting a computing interface comprising the multiple images of the cells of the first portion of the blood sample and the one or more numerical parameters of the cells of the second portion of the blood sample.

[0326] Example 17B

[0327] A sample analysis method according to Example 16B, wherein the method includes separating a blood sample into a plurality of aliquots using an aliquoter, wherein the first portion is a first aliquot of the plurality of aliquots and the second portion is a second aliquot of the plurality of aliquots.

[0328] Example 18B

[0329] The sample analysis method according to example 16B, wherein the method comprises: a) receiving a blood sample in a container with a barcode; b) reading the barcode; and c) determining one or more tests for the blood sample based on the barcode.

[0330] Example 19B

[0331] A sample analysis method according to Example 16B, wherein: a) the fluid system is used to flow a first subportion of a first portion of the blood sample through a flow imaging module for red blood cell (RBC) imaging in a first flow pool; and b) the fluid system is used to flow a second subportion of the first portion of the blood sample through the flow imaging module for white blood cell (WBC) imaging, the second subportion being treated with a stain composition.

[0332] Example 20B

[0333] The sample analysis method according to Example 16B, wherein the second module includes an impedance analyzer.

[0334] Example 21B

[0335] The sample analysis method according to Example 16B, wherein the second module includes a fluorescence analyzer.

[0336] Example 22B

[0337] The sample analysis method according to Example 16B, wherein the numerical parameter is selected from the group consisting of mean corpuscular volume, cell count, and hemoglobin concentration.

[0338] Example 23B

[0339] The sample analysis method according to Example 16B, wherein the plurality of images includes images of a first cell type and images of a second cell type.

[0340] Example 24B

[0341] The sample analysis method according to Example 16B, wherein the plurality of cells includes a first cell type, and wherein 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.

[0342] Example 25B

[0343] A sample analysis method according to Example 16B, wherein the plurality of cells includes a first cell type and a second cell type, and wherein the computing interface is configured to allow a user to select the first cell type and the second cell type and in response display an image of the first cell type and an image of the second cell type.

[0344] Example 26B

[0345] The sample analysis method according to 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 a computing interface.

[0346] Example 27B

[0347] The sample analysis method according to Example 16B, wherein the second module is further configured to test one or more numerical parameters of the first cell type and to test one or more numerical parameters of the second type.

[0348] Example 28B

[0349] The sample analysis method according to Example 16B, wherein the second module is configured to determine more than one parameter of the first cell type.

[0350] Example 29B

[0351] The sample analysis method according to Example 16B, wherein the computing interface is configured to provide a plurality of images of cells of a first portion of the blood sample and one or more numerical parameters of a second portion of the blood sample on a single screen.

[0352] Example 30B

[0353] A sample analysis method according to Example 16B, wherein: a) the one or more processors include: i) a first processor programmed to determine one or more parameters of cells of a second portion of the blood sample; and ii) a second processor programmed to present a computing interface comprising a plurality of images of cells of the first portion of the blood sample and one or more numerical parameters of cells of the second portion of the blood sample; b) the second processor constitutes an analyzer that also includes a fluid system; and c) the first processor does not constitute an analyzer and is separated from and communicates with the second processor via a wide area network.

[0354] Example 1C

[0355] A sample analysis system comprises: a) a fluid system for: i) flowing a first portion of a blood sample through a first module, the first module being a flow imaging module comprising a flow pool and an image capture device, the image capture device being configured to capture multiple images of a first type of cells; and ii) flowing 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 cells; b) one or more processors programmed to: i) determine one or more image-based numerical values of the first cell type from the multiple images from the first module; ii) determine one or more numerical parameters of the first cell type from the second module; and iii) presenting a computational interface comprising 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.

[0356] Example 2C

[0357] The sample analysis system according to Example 1C, wherein the first cell type is red blood cells or platelets.

[0358] Example 3C

[0359] A sample analysis system according to Example 1C, wherein the fluid system is used to capture multiple images of a second type of cell and test one or more numerical parameters of the second type of cell, and wherein one or more processors are programmed to: determine one or more image-based numerical values of the second cell type from the multiple images from the first module, determine one or more numerical parameters of the second cell type from the second module, and present a computational 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 second cell type.

[0360] Example 4C

[0361] The sample analysis system according to Example 3C, wherein the first cell type is red blood cells and the second cell type is platelets.

[0362] Example 5C

[0363] The sample analysis system according to example 1C, wherein the first cell type is red blood cells, 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.

[0364] Example 6C

[0365] The sample analysis system according to Example 1C, wherein the second module includes an impedance analyzer.

[0366] Example 7C

[0367] The sample analysis system according to Example 1C, wherein the second module includes a fluorescence analyzer.

[0368] Example 8C

[0369] The sample analysis system according to Example 1C, wherein the second module includes a spectrophotometric analyzer.

[0370] Example 9C

[0371] The sample analysis system according to Example 1C, 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.

[0372] Example 10C

[0373] The sample analysis system according to Example 1C, wherein the sample analysis system comprises: 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.

[0374] Example 11C

[0375] The sample analysis system according to Example 10, wherein the sample analysis system includes an aliquoter configured to separate the sample into aliquots; and wherein the controller is programmed to cause the fluid system to control the flow of the aliquots based on a parameter for determining a value.

[0376] Example 12C

[0377] The sample analysis system of Example 1C, wherein the one or more processors are programmed to present a computational interface on a single screen that includes one or more image-based numerical values of a first cell type and one or more numerical parameters of the first cell type.

[0378] Example 13C

[0379] The sample analysis system according to Example 1C, wherein the first cell type is red blood cells, the one or more image-based parameters include a red blood cell count, and the one or more numerical parameters include a hemoglobin measurement.

[0380] Example 14C

[0381] The sample analysis system according to Example 1C, wherein the computing interface is configured to enable a user to select a first cell type, and then in response display a plurality of images of the first cell type.

[0382] Example 15C

[0383] A sample analysis system according to 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 multiple images from a first module; and ii) a second processor programmed to present a computational interface comprising the 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 constitutes an analyzer that also includes a fluid system; c) the first processor does not constitute an analyzer and is separated from and communicates with the second processor via a wide area network.

[0384] Example 16C

[0385] A method for analyzing a sample comprises: a) using a fluid system: i) flowing a first portion of a blood sample through a first module, the first module being a flow imaging module comprising a flow pool and an image capture device, the image capture device being configured to capture multiple images of a first type of cell; and ii) flowing 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: i) determining one or more image-based numerical values of the first cell type from the multiple images from the first module; ii) determining one or more numerical parameters of the first cell type from the second module; and iii) presenting a computing interface comprising 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.

[0386] Example 17C

[0387] The sample analysis method according to Example 16C, wherein the first cell type is red blood cells or platelets.

[0388] Example 18C

[0389] A sample analysis method according to Example 16C, wherein the fluid system is used to capture multiple images of a second type of cell and test one or more numerical parameters of the second type of cell, and wherein one or more processors are programmed to determine one or more image-based numerical values of the second cell type from the multiple images from the first module, determine one or more numerical parameters of the second cell type from the second module, and present a computational interface that includes the one or more image-based numerical values of the first cell type and one or more numerical parameters of the second cell type.

[0390] Example 19C

[0391] The sample analysis method according to Example 18C, wherein the first cell type is red blood cells and the second cell type is platelets.

[0392] Example 20C

[0393] The sample analysis method according to Example 16C, wherein the first cell type is red blood cells, 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.

[0394] Example 21C

[0395] The sample analysis method according to Example 16C, wherein the second module includes an impedance analyzer.

[0396] Example 22C

[0397] The sample analysis method according to Example 16C, wherein the second module includes a fluorescence analyzer.

[0398] Example 23C

[0399] The sample analysis method according to Example 16C, wherein the second module includes a spectrophotometer.

[0400] Example 24C

[0401] The sample analysis method according to 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.

[0402] Example 25C

[0403] The sample analysis method according to Example 16C, 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.

[0404] Example 26C

[0405] A sample analysis method according to Example 25C, wherein the sample analysis system includes an aliquoter configured to separate the sample into aliquots, and wherein the controller is programmed to cause the fluid system to control the flow of the aliquots based on a parameter for determining a value.

[0406] Example 27C

[0407] The sample analysis method of Example 16C, wherein the one or more processors are programmed to present a computational interface on a single screen that includes one or more image-based numerical values of the first cell type and one or more numerical parameters of the first cell type.

[0408] Example 28C

[0409] The sample analysis method according to Example 16C, wherein the first cell type is red blood cells, the one or more image-based parameters include a red blood cell count, and the one or more numerical parameters include a hemoglobin measurement.

[0410] Example 29C

[0411] The sample analysis method according to Example 16C, wherein the computing interface is configured to cause a user to select a first cell type, and then in response display a plurality of images of the first cell type.

[0412] Example 30C

[0413] A sample analysis method according to Example 16C, wherein: a) 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 multiple images from a first module; and ii) a second processor programmed to present a computing interface that includes the 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 constitutes an analyzer that also includes a fluid system; c) the first processor does not constitute an analyzer and is separated from the second processor by a wide area network and communicates with the second processor via the wide area network.

[0414] explain

[0415] It should be understood that in the above examples and claims, the statement that something is "based on" something else should be understood to mean that it is at least partially determined by the thing it is indicated as being based on. In order to indicate that something must be completely determined based on something else, it is described as being "completely based on" whatever it must be completely determined by.

[0416] It should be understood that the statement that "one or more" or "at least one" of one type of item has a certain characteristic indicates that the items in the indicated group have the characteristic in common. To indicate that each item in the group has a certain characteristic, the phrase "each" will be used with a group identifier (e.g., "one or more" or "at least one").

[0417] It should be understood that in the claims, "a set" should be understood to mean one or more things of similar nature, design or function.

[0418] It should be understood that any of the examples described herein may include various other features in addition to or in place of those described above. By way of example only, any of the examples described herein may also include one or more of the various features disclosed in any of the various references incorporated herein by reference.

[0419] It should be understood that any one or more of the teachings, expressions, embodiments, examples, etc. described herein can be combined with any one or more of the other teachings, expressions, embodiments, examples, etc. described herein. Therefore, the above teachings, expressions, embodiments, examples, etc. should not be viewed in isolation from each other. In view of the teachings herein, various suitable ways in which the teachings herein can be combined will be readily apparent to those of ordinary skill in the art. Such modifications and variations are intended to be included within the scope of the claims.

[0420] It should be understood that any patent, publication, or other public material allegedly incorporated herein by reference, in whole or in part, is incorporated herein only to the extent that the incorporated material does not conflict with existing definitions, statements, or other public material set forth in the present disclosure. Accordingly, and to the extent necessary, the disclosure explicitly set forth herein supersedes any conflicting material incorporated herein by reference. Any material, or portion thereof, allegedly incorporated herein by reference that conflicts with existing definitions, statements, or other public material set forth herein is incorporated only to the extent that there is no conflict between the incorporated material and existing public material.

[0421] While various forms of the present invention have been shown and described, further improvements to 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. Several such potential modifications have been mentioned, and other modifications will be apparent to those skilled in the art. For example, the examples, forms, geometries, materials, dimensions, ratios, steps, etc. discussed above are illustrative and not required. Accordingly, the scope of the present invention should be considered in light of the appended claims and should be understood not to be limited to the details of construction and operation shown and described in the specification and drawings.

Claims

1. A sample analysis system comprising: a) Fluid systems for: i) flowing a first portion of the 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 of a first type; as well as ii) passing a second portion of the blood sample through a second module configured to test one or more numerical parameters of cells of the first type; b) one or more processors programmed to: i) determining one or more image-based values of a first cell type from the plurality of images from the first module; ii) determining one or more numerical parameters of the first cell type from said second module; iii) presenting a computational interface comprising one or more image-based numerical values of said first cell type and one or more numerical parameters of said first cell type.

2. The sample analysis system according to claim 1, wherein: The first cell type is a red blood cell or a platelet.

3. The sample analysis system according to claim 1, wherein: The fluid system is used to capture multiple images of a second type of cell and test one or more numerical parameters of the second type of cell, and wherein the one or more processors are programmed to: determine one or more image-based numerical values of the second cell type from the multiple 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 that includes 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.

4. The sample analysis system according to claim 3, wherein: The first cell type is red blood cells and the second cell type is platelets.

5. The sample analysis system according to claim 1, wherein: The first cell type is red blood cells, 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. The sample analysis system according to claim 1 , wherein: The second module includes an impedance analyzer.

7. The sample analysis system according to claim 1, wherein: The second module includes a fluorescence analyzer.

8. The sample analysis system according to claim 1, wherein: The second module includes a spectrophotometric analyzer.

9. The sample analysis system according to claim 1, 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.

10. The sample analysis system according to claim 1, 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. The sample analysis system according to claim 10 , wherein: The sample analysis system includes an aliquoter configured to separate a sample into aliquots, and wherein the controller is programmed to cause the fluid system to control a flow of the aliquots based on a parameter used to determine a value.

12. The sample analysis system according to claim 1, wherein: The one or more processors are programmed to present a computational interface on a single screen that includes one or more image-based numerical values of the first cell type and one or more numerical parameters of the first cell type.

13. The sample analysis system according to claim 1, wherein: The first cell type is red blood cells, the one or more image-based parameters include a red blood cell count, and the one or more numerical parameters include a hemoglobin measurement.

14. The sample analysis system according to claim 1, wherein: The computing interface is configured to enable a user to select the first cell type, and then in response display the plurality of images of the first cell type.

15. The sample analysis system according to claim 1, wherein: a) the one or more processors comprising: i) a first processor programmed to determine one or more image-based values of the first cell type from the plurality of images from the first module; and ii) a second processor programmed to present the computational interface comprising 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 constitutes an analyzer, the analyzer also comprising the fluid system; c) The first processor does not constitute the analyzer and is separated from the second processor by a wide area network and communicates with the second processor via the wide area network.

16. A sample analysis method comprising: a) Using fluid system: i) flowing a first portion of the 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 of a first type; as well as ii) passing a second portion of the blood sample through a second module configured to test one or more numerical parameters of cells of the first type; as well as b) Using one or more processors: i) determining one or more image-based values of a first cell type from the plurality of images from the first module; ii) determining one or more numerical parameters of the first cell type from said second module; iii) presenting a computational interface comprising one or more image-based numerical values of said first cell type and one or more numerical parameters of said first cell type.

17. The sample analysis method according to claim 16, wherein: The first cell type is a red blood cell or a platelet.

18. The sample analysis method according to claim 16, wherein: The fluid system is used to capture multiple images of a second type of cells and test one or more numerical parameters of the second type of cells, and wherein the one or more processors are programmed to determine one or more image-based numerical values of the second cell type from the multiple 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 that includes 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.

19. The sample analysis method according to claim 18, wherein: The first cell type is red blood cells and the second cell type is platelets.

20. The sample analysis method according to claim 16, wherein: The first cell type is red blood cells, 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.

21. The sample analysis method according to claim 16, wherein: The second module includes an impedance analyzer.

22. The sample analysis method according to claim 16, wherein: The second module includes a fluorescence analyzer.

23. The sample analysis method according to claim 16, wherein: The second module includes a spectrophotometric analyzer.

24. The sample analysis method according to claim 16, 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.

25. The sample analysis method according to claim 16, 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.

26. The sample analysis method according to claim 25, wherein: The sample analysis system includes an aliquoter configured to separate a sample into aliquots, and wherein the controller is programmed to cause the fluid system to control a flow of the aliquots based on a parameter used to determine a value.

27. The sample analysis method according to claim 16, wherein: The one or more processors are programmed to present a computational interface on a single screen that includes one or more image-based numerical values of the first cell type and one or more numerical parameters of the first cell type.

28. The sample analysis method according to claim 16, wherein: The first cell type is red blood cells, the one or more image-based parameters include a red blood cell count, and the one or more numerical parameters include a hemoglobin measurement.

29. The sample analysis method according to claim 16, wherein: The computing interface is configured to enable a user to select the first cell type, and then in response display a plurality of images of the first cell type.

30. The sample analysis method according to claim 16, wherein: a) the one or more processors comprising: i) a first processor programmed to determine one or more image-based values of the first cell type from the plurality of images from the first module; and ii) a second processor programmed to present the computational interface comprising 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 constitutes an analyzer, the analyzer also comprising the fluid system; c) The first processor does not constitute the analyzer and is separated from the second processor by a wide area network and communicates with the second processor via the wide area network.

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