Imaging techniques to determine blood cell characteristics

Abstract

Cell analysis methods may include, and cell analysis systems may be configured to support, flowing blood cells from a sample through a flow cell, capturing images of the cells as they flow through the flow cell, and then processing them based on execution of instructions stored on a non- transitory computer readable medium. This processing may include determining a plurality of cell paramctcr values for each cell image, and, for each of the cell images, determining a morphology score for that image based on that image's plurality of cell parameter values.

Description

IMAGING TECHNIQUES TO DETERMINE BLOOD CELLCHARACTERISTICSBACKGROUND

[0001] Determining the morphology of blood cells (e.g., red blood cells) in a blood sample can be useful for various reasons, including in ascertaining patient health. However, this type of morphology assessment can be difficult to perform in an automated manner.

[0002] In traditional methods of blood analysis, indirect techniques of measurement such as lightscattering analysis, fluorescence, or impedance-based methodologies were used. These indirect techniques have use in establishing certain types of information about cells, but have limitations including in ascertaining information about cell morphology. Newer classes of analyzers may use imaging to take images of blood cells, these images can be useful to obtain additional information about blood cells beyond the capabilities of traditional technologies, such as information pertaining to cell morphology. However, it can be difficult to determine which image characteristics are useful to obtain relevant information about cell morphology, and how these characteristics can be used together to cell morphology analysis.

[0003] There is therefore a need for cell morphology assessment methods, systems, devices, and / or algorithms utilizing imaging related data.SUMMARY

[0004] Aspects of the present disclosure may be used in determining the morphology of blood cells in hematology flow imaging analyzers.

[0005] A first aspect of the technology described herein refers to a cell analysis method comprising: causing blood cells from a sample to be flowed through a flow cell; causing a plurality of cell images to be captured by using a camera, a plurality of cell images, wherein each cell imagefrom the plurality of cell images depicts a respective cell as it is flowing through the flow cell; for each cell image from the plurality of cell images, determining, by a computing device, a plurality of cell parameter values for that cell image; and for each cell image from the plurality of cell images, determining, by the computing device, a morphology score for that image based on the plurality of cell parameter values for that cell image.

[0006] In particular, causing the blood cells from the sample to be flowed through the flow cell comprises flowing the blood cells from the sample through the flow cell. For instance, causing the blood cells from the sample to be flowed through a flow cell comprises instructing, e.g., by the computing device, an analyzer comprising the flow cell to flow the blood cells from the sample through a flow cell. Causing the blood cells from the sample to be flowed through the flow cell may be carried out by the computing device.

[0007] In particular, causing the plurality of cell images to be captured comprises capturing, by using the camera, the plurality of cell images. For example, causing the plurality of cell images to be captured comprises instructing, e.g., by the computing device, the camera to capture the plurality of cell images. Causing the plurality of cell images to be captured may be carried out by the computing device.

[0008] The camera may be a photo camera, a video camera or the like. For instance, the camera is a device configured to capture image, in particular a digital image, more particularly a digital image of a cell. The images of the plurality of cell images may be represented by using the RGB color format. In particular, the images of the plurality of cell images may be represented by using the Hue, Saturation, Lightness (HSL) representation or the Hue, Saturation, Brightness (HSB) representation.

[0009] In particular’, determining the plurality of cell parameter values of a cell image may comprise processing the cell image, e.g., the pixels thereof, according to an image processing procedure and thereby obtaining the plurality of cell parameter values. For instance, determining the plurality of cell parameter values of a cell image may comprise identifying a foreground insaid cell image based on a lightness of each pixel in the image and determining the plurality of cell parameter values by using the foreground of the image, c.g. of the pixels of that foreground.

[0010] In particular, in some embodiments of the technology described herein, the foreground of the image is a set of pixels of the image. According to some embodiments of the described technology, identifying the foreground in an image may comprise selecting the pixels of the image that have a lightness value greater than or equal to a lightness threshold. In some cases, e.g. if the images of the plurality of cell images are represented in the RBG color format and / or in the HSV representation, identifying a foreground in a cell image may comprise converting said cell image in the HSL representation.

[0011] A cell parameter value of the plurality of cell parameter values may comprise information indicative of the dimensions of the cell. For instance, a cell parameter value of the plurality of cell parameter values may be the cell area of the cell. In particular, a cell parameter value of the plurality of cell parameter values may comprise information indicative of the form or shape of the cell. For example, a cell parameter value of the plurality of cell parameter values may be the eccentricity of the cell.

[0012] In particular, determining the morphology score for a cell image based on the plurality of cell parameter values for that cell image may comprise processing said plurality of cell parameter values according to a cell parameters processing procedure and thereby obtaining the morphology score.

[0013] According to the disclosed technology, the computing device may comprise one or more processors. For instance, a processor may be a CPU or a GPU. Additionally, the computing device may comprise a memory. For example, the computing device may be a computer, a laptop, a smartphone, a tablet and the like. According to aspects of the technology described herein, a computing device may be a distributed system such as a computer network and / or a cloud computing architecture. In particular, the memory of the computing device comprises computer executable instructions which, when executed by the computing device, cause the computing device to: determine, for each cell image from the plurality of cell images, theplurality of cell parameter values for that cell image; and determine, for each cell image from the plurality of cell images, the morphology score for that image based on the plurality of cell parameter values for that cell image.

[0014] Exemplarily, the sample is a biological sample. Alternatively or in addition, for each image from the plurality of cell images, the cell depicted in that cell image may be a red blood cell and the morphology score indicates a front facing orientation of the red blood cell depicted in that cell image relative to the camera.

[0015] A biological sample may be a sample of a bodily fluid of a human or animal subject. For example, the bodily fluid may be a physiological fluid, such as blood, saliva, urine, sweat, amniotic fluid, cerebrospinal fluid, ascites fluid, or the like. The biological sample may comprise one or more components that may potentially comprise analytes of interest for performing at least one clinical test. For instance, blood serum, blood cells and blood plasma are components of a blood sample.

[0016] Exemplarily, for each image from the plurality of cell images, the cell depicted in that cell image may be a red blood cell and the morphology score indicates a spatial orientation of the red blood cell depicted in that cell image relative to the camera.

[0017] In particular, the morphology score may be a value comprised between 0 and 1. In this case, for instance, if the morphology score for an image is greater than or equal to 0.5, the cell depicted in this image is considered to front face the camera, i.e. to be oriented in a front facing orientation relative to the camera. In particular, if, instead, the morphology score for an image is lower than to 0.5, the cell depicted in this image is considered not to be a red blood cell that front faces the camera.

[0018] Typically, a Red Blood Cell (RBC) has a generally biconcave shape comprising two opposite facing concave external surface regions. In particular, each of the two concave external surface regions are substantially perpendicular to a traversal axis of the RBC. As set forth herein, a front-facing orientation of the RBC is in particular the orientation in which one of the twoconcave external surface regions faces the camera. For example, a non front-facing orientation of the RBC is an orientation such that the traversal axis of the RBC is parallel to a field of view of the camera.

[0019] For instance, for each image from the plurality of cell images, determining the morphology score for that cell image comprises providing the plurality of cell parameter values for that image as input to a morphology score generation function.

[0020] Exemplarily, for each image from the plurality of cell images, the plurality of cell parameter values for that cell image comprises two or more of: an area of the cell depicted in that image, a solidity of the cell depicted in that image, an eccentricity of the cell depicted in that image, and an equivalent diameter of the cell depicted in that image.

[0021] In particular, for each image from the plurality of cell images, the plurality of cell parameter values for that cell image comprises: the area of the cell depicted in that image, the solidity of the cell depicted in that image, the eccentricity of a cell depicted in that image, the equivalent diameter of the cell depicted in that image, an area of a bounding box of the cell depicted in that image, and a circular shape of the cell depicted in that image.

[0022] Exemplarily, the morphology score generation function comprises a function which is directly proportional to a product of first and second inputs and is inversely proportional to a product of a third input and a fourth input, wherein, for each image from the plurality of cell images, providing the plurality of cell parameter values for that cell image as input to the morphology score generation function comprises providing the area of the cell depicted in that image as the first input, providing the equivalent diameter of the cell depicted in that image as the second input, providing the eccentricity of the cell depicted in that image as the third input, and providing the circular shape of the cell depicted in that image as the fourth input.

[0023] In some embodiments, the morphology score generation function, F, reads as follows:wherein Ij (J = 1,2, 3, 4) is the jthinput.

[0024] In some embodiments, the function is also directly proportional to one or more of a fifth input and a sixth input. If this is the case, in particular, the for each image from the plurality of cell images, providing the plurality of cell parameter values for that cell image as input to the morphology score generation function comprises providing one or more of the solidity of the cell depicted in that image as the third input and the area of the bounding box f the cell depicted in that image as the fourth input. For instance, the morphology score generation function, F, may read as follows:wherein / 5and / 6are respectively the fifth and the sixth input.

[0025] The method according to the first aspect of the disclosed technology, may further comprise determining, for each cell image from the plurality of cell images, a plurality of additional parameter values for that cell image, the plurality of additional parameter values being other than the plurality of cell parameter values. For example, for each image from the plurality of cell images, the plurality of additional parameter values for that cell image comprises one or more of a ratio of blueness to redness in that cell image or a granularity of the nucleus of the cell depicted in that image.

[0026] Alternatively or in addition, a method implemented based on this disclosure may comprise determining, based on the morphology scores for the plurality of cell images, an alignment characteristic score of an analyzer comprising the flow cell. In particular, the analyzer further comprises the camera. More particularly, the analyzer further comprises the computing device.

[0027] For example, the alignment characteristic score comprises information specifying one or more of (i) an estimate of an amount of front facing cells in the sample and (ii) an estimate of a fraction of front facing cells in the sample.

[0028] In particular, determining the alignment characteristic score of the analyzer comprises obtaining, e.g., counting, the number, NF, of images of the plurality of cell images that, according to the morphology scores, depict cells oriented in a front facing orientation relative to the camera. Additionally, determining the alignment characteristic score of the analyzer may comprise computing the alignment characteristic score, A, e.g., according to the following formula:wherein N is the number of images of the plurality of cell images.

[0029] Exemplarily, determining the alignment characteristic score of the analyzer comprises obtaining, e.g., counting, the number, Ns, of images of the plurality of cell images that, according to the morphology scores, do not depict a red blood cell oriented in a front facing orientation relative to the camera. Additionally, determining the alignment characteristic score of the analyzer may comprise computing the alignment characteristic score, A, e.g., according to the following formula:

[0030] In some embodiments, computing the alignment characteristic score, A, may be carried out by using the following formula: A = Ns / N.

[0031] Exemplarily, the alignment characteristic score of the analyzer (hereinafter also referred to as: “first analyzer”) comprises a percentage of front facing cells. For instance, computing the alignment characteristic score, A, may be carried out by using the following formula: A = NP / N X 100.

[0032] In particular, a method implemented based on this disclosure may further comprise: presenting a graphical interface comprising a plot displaying a plurality of points, wherein, for each pointfrom the plurality of points, that point corresponds to a respective cell image from the plurality of cell images, wherein, for each point from the plurality of points, that point is displayed at a location determined based on the plurality of cell parameter values for that cell image.

[0033] A second aspect of the disclosed technology provides a cell analysis system comprising a flow cell, a camera and a computing device. For instance, the cell analysis system according to the second aspect of the disclosed technology is configured to carry out the method according to the first aspect of the disclosed technology. In particular, the cell analysis system is configured to: flow blood cells from a blood sample through the flow cell; capture, by using the camera, a plurality of cell images, wherein each cell image from the plurality of cell images depicts a cell as it is flowing through the flow cell; for each cell image from the plurality of cell images, determine, by the computing device, a plurality of cell parameter values for that cell image; and for each cell image from the plurality of cell images, determine, by the computing device, a morphology score for that image based on the plurality of cell parameter values for that cell image.

[0034] In particular, the flow cell and the camera are arranged with respect to one another so that the flow cell is configured to convey at least a portion of the sample fluid through a viewing zone of the camera.

[0035] In particular, for each image from the plurality of cell images, the cell depicted in that cell image is a red blood cell. For instance, the morphology score indicates a front facing orientation of the red blood cell relative to the camera.

[0036] Exemplarily the system according to the second aspect of the disclosed technology may be further configured to determine, by the computing device and for each image from the plurality of cell images, the morphology score for that cell image at least by providing the plurality of cell parameter values for that image as input to a morphology score generation function.

[0037] In particular, for each image from the plurality of cell images, the plurality of cell parameters values for that cell image comprises two or more of: the area of the cell depicted in that image,the solidity of the cell depicted in that image, the eccentricity of the cell depicted in that image, and the equivalent diameter of the cell depicted in that image.

[0038] Exemplarily, for each image from the plurality of cell images, the plurality of cell parameter values for that cell image comprise: the area of the cell depicted in that image, the solidity of the cell depicted in that image, the eccentricity of a cell depicted in that image, the equivalent diameter of the cell depicted in that image, the area of a bounding box of the cell depicted in that image, and the circular shape of the cell depicted in that image.

[0039] In particular, the morphology score generation function comprises a function which is directly proportional to a product of first and second inputs and is inversely proportional to product of a third input and a fourth input, wherein, for each image from the plurality of cell images, providing the plurality of cell parameter values for that cell image as input to the morphology score generation function comprises providing the area of the cell depicted in that image as the first input, providing the equivalent diameter of the cell depicted in that image as the second input, providing the eccentricity of the cell depicted in that image as the fourth input, and providing the circular shape of the cell depicted in that image as the fifth input.

[0040] In some embodiments, the function is also directly proportional to one or more of a fifth input and a sixth input. If this is the case, in particular, then for each image from the plurality of cell images, providing the plurality of cell parameter values for that cell image as input to the morphology score generation function comprises providing one or more of the solidity of the cell depicted in that image as the third input and the area of the bounding box of the cell depicted in that image as the fourth input.

[0041] The system according to the second aspect of the disclosed technology may be further configured to determine, by the computing device and for each cell image from the plurality of cell images, the plurality of additional parameter values for that cell image, the plurality of additional parameter values being other than the plurality of cell parameter values.

[0042] In particular, the system is further configured to obtain, based on the morphology scores for the plurality of cell images, an alignment characteristic score of the cell analysis system.

[0043] For instance, the alignment characteristic score of the cell analysis system may comprise a percentage of front facing cells.

[0044] Exemplarily, the system according to the second aspect of the disclosed technology is further configured to present a graphical interface comprising a plot displaying a plurality of points, wherein, for each point from the plurality of points, that point corresponds to a cell image from the plurality of cell images, wherein, for each point from the plurality of points, that point is displayed at a location determined based on the plurality of cell parameter values for that cell image.

[0045] In particular, the system may be further configured to determine, based on the plot, a design change in an analyzer comprising the flow cell.

[0046] A third aspect of the technology disclosed herein refers to a computing device comprising a processor configured to perform the method according to the first aspect of the disclosed technology.

[0047] A fourth aspect of the technology disclosed herein refers to a computer program product comprising instructions which, when the program is executed by a computer, cause the computer to carry out the steps of the method according to the first aspect of the disclosed technology.

[0048] A fifth aspect of the technology disclosed herein refers to a computer-readable storage medium comprising instructions which, when executed by a computer, cause the computer to cany out the steps of the method according to the first aspect of the disclosed technology.

[0049] It should be noted that any of the various features of the aspects disclosed herein can be included or combined in each of those aspects. Other aspects and implementations aredescribed herein. Accordingly, the exemplary aspects described in this summary should be understood as being illustrative only and should not be treated as limiting.BRIEF DESCRIPTION OF THE DRAWINGS

[0050] While the specification concludes with claims which particularly point out and distinctly claim the invention, it is believed the present invention will be better understood from the following description of certain examples taken in conjunction with the accompanying drawings, in which like reference numerals identify the same elements and in which:

[0051] FIG. 1 is a schematic illustration, partly in section and not to scale, showing operational aspects of an exemplary flow cell which may be used in an analyzer configured to capture and analyze images.

[0052] FIG. 2 illustrates an example method which may be used to analyze images of particles (e.g., cells) in a sample.

[0053] FIG. 3 illustrates a method which may be used in evaluating an analyzer’s design.

[0054] FIG. 4 illustrates an example interface which could be presented to a user in connection with evaluation of an analyzer.

[0055] FIG. 5 illustrates images of cells which are front facing.

[0056] FIG. 6 illustrates images of cells which are not front facing.

[0057] The drawings are not intended to be limiting in any way, and it is contemplated that various embodiments of the invention may be carried out in a variety of other ways, including those not necessarily depicted in the drawings. The accompanying drawings incorporated in and forming a part of the specification illustrate several aspects of the present invention, and together with the description serve to explain the principles of the invention; it being understood, however, that this invention is not limited to the precise arrangements shown.DETAILED DESCRIPTION

[0058] The present disclosure relates to articles, systems, and methods for determining a morphology score of cells which are imaged by an automated analyzer. In some aspects, the analyzers may be visual analyzers comprising processors to facilitate automated conversion and / or analysis of images. Such analyzers may be useful, for example, in characterizing particles in biological fluids, such as detecting and quantifying erythrocytes, reticulocytes, nucleated red blood cells, platelets, and white blood cells, including white blood cell differential counting, categorization and subcategorization and analysis. Other similar uses such as characterizing blood cells from other fluids (serum, bone marrow, lavage fluid, effusions, exudates, cerebrospinal fluid, pleural fluid, peritoneal fluid, and amniotic fluid) are also contemplated.

[0059] Turning now to the drawings, FIG. 1 schematically shows an exemplary flow cell 22 which may be used in an analyzer for conveying a sample fluid through a viewing zone 23 of a high optical resolution imaging device 24 (e.g., a camera) in a configuration for imaging microscopic particles in a sample flow stream 32 using digital image processing. Flow cell 22 is coupled to a source 25 of sample fluid which may have been subjected to processing, such as contact with a particle contrast agent composition and heating. Flow cell 22 is also coupled to one or more sources 27 of a particle and / or intracellular organelle alignment liquid (PIOAL) / sheath fluid, such as a clear glycerol solution having a viscosity that is greater than the viscosity of the sample fluid, an example of which is disclosed in U.S. Pat. Nos. 9,316,635 and 10,451,612, the disclosures of which are hereby incorporated by reference in their entirety.

[0060] The sample fluid is injected through a flattened opening at a distal end 28 of a sample feed tube 29, and into the interior of the flow cell 22 at a point where the PIOAL flow has been substantially established resulting in a stable and symmetric laminar flow of the PIOAL above and below (or on opposing sides of) the ribbon-shaped sample stream. The sample and PIOAL streams may be supplied by precision metering pumps that move the PIOAL with the injected sample fluid along a flow path that narrows substantially. The PIOAL envelopes and compresses the sample fluid in the zone 21 where the flow path narrows. Hence, the decreasein flow path thickness at zone 21 can contribute to a geometric focusing of the sample flow stream 32. The sample flow stream 32 is enveloped and carried along with the PIOAL downstream of the narrowing zone 21, passing in front of, or otherwise through the viewing zone 23 of, the high optical resolution imaging device 24 where images are collected, for example, using a Charge-Coupled Device (CCD) 48 observing the sample stream as illuminated using an illumination source 42 through a viewing port 57. Processor 18 can receive, as input, pixel data from CCD 48. The sample fluid ribbon flows together with the PIOAL to a discharge 33.

[0061] As shown here, the narrowing zone 21 can have a proximal flow path portion 21a having a proximal thickness PT and a distal flow path portion 21b having a distal thickness DT, such that distal thickness DT is less than proximal thickness PT. The sample fluid can therefore be injected through the distal end 28 of sample tube 29 at a location that is distal to the proximal portion 21a and proximal to the distal portion 21b. Hence, the sample fluid can enter the PIOAL envelope as the PIOAL stream is compressed by the zone 21 , wherein the sample fluid injection tube has a distal exit port through which sample fluid is injected into flowing sheath fluid, the distal exit port bounded by the decrease in flow path size of the flow cell.

[0062] The digital high optical resolution imaging device 24 with objective lens 46 is directed along an optical axis that intersects the ribbon-shaped sample flow stream 32. The relative distance between the objective 46 and the flow cell 33 is variable by operation of a motor drive 54, for resolving and collecting a focused digitized image on a photosensor array. Additional information regarding the construction and operation of an exemplary flow cell such as shown in FIG. 1 is provided in U.S. Patent 9,322,752, entitled “Flow cell Systems and Methods for Particle Analysis in Blood Samples,” filed on March 17, 2014, the disclosure of which is hereby incorporated by reference in its entirety.

[0063] In operation, an analyzer such as an analyzer incorporating a flow cell based imaging system as illustrated in FIG. 1 may analyze images of particles (e.g., cells) in a sample, such as by using a process of the type shown in FIG. 2. Initially, in the process of FIG. 2, cells from asample (e.g., a blood sample or other type of biological sample) are flowed 201 through a flow cell (e.g., the flow cell 22 depicted in FIG. 1). As the cells arc being flowed 201 through the flow cell, a plurality of cell images may be captured 202 (e.g., using a camera 24 as described in the context of FIG. 1), in which each cell image may depict a respective cell as it is flowing through the flow cell. Examples of these types of cell images are provided in FIGS. 5-6, in which FIG. 5 depicts images of cells which are front facing, and FIG. 6 depicts images of cells which are not front facing. These cell images may then proceed to be processed 203 to determine a set of parameter values, or may be subject to some additional preprocessing before the parameter values are determined 203. For example, in some cases, a camera 24 may capture a superset of cell images, and that superset of cell images may be filtered to remove images which are out of focus so that the plurality of images which are processed 203 to determine cell parameter values would include only images which are sufficiently in focus. This may involve determining focus values using a Laplacian transform as described in J.L. Pech- Pacheco et. al., Diatom autofocusing in brightfield microscopy: a comparative study, Proceedings 15th International Conference on Pattern Recognition. 1CPR-2000, Barcelona, Spain, 2000, pp. 314-317 vol.3, available at determining focus using contrast such asdescribed in U.S. Patent 9,857,361 entitled “Flowcell, Sheath fluid, and Autofocus Systems and Methods for Particle Analysis in Urine Samples”, U.S. Patent 10,705,008 entitled “Autofocus Systems and Methods for Particle Analysis in Blood Samples,” international application PCT / US2023 / 011759 entitled “Measure Image Quality of Blood Cell Images,” each of which is hereby incorporated by reference in its entirety, and then comparing those determined focus values with a threshold which had been previously determined for identifying images which were suitable for being further processed as described herein.

[0064] However it takes place, once cell images had been captured 202, they may then be processed to determine 203 a set of cell parameter values for each image. This processing may include, for example, separating the foreground from the background of each image using thresholding, e.g., the Otsu’s method. In this type of embodiment, after separating the foreground andbackground, the cell parameter values for an image may be determined 203 by analyzing the foreground portion of that image. For example, table 1, below, illustrates exemplary cell parameters as well as how values for those parameters may be determined:Table 1: Exemplary cell parameters and how their values may be calculated.

[0065] In particular, one or more of the Cell Area, the Hull area, the Equivalent diameter, the Bounding Box, the Bounding Box Area, the Eccentricity mentioned above may be defined according to the definitions of the scikit-image python package, e.g. of version 0.21.0 thereof. In particular, one or more of the Cell Area, the Hull area, the Solidity, the Equivalent diameter, the Bounding Box, the Bounding Box Area, the Eccentricity mentioned above may be computed by using of the scikit-image python package, e.g. of version 0.21.0 thereof. For instance, in some embodiments, exemplary cell parameter values that may be provided by the determination of the plurality of cell parameter values 203, as well as properties of the scikit- image python package that may be used to estimate them, are provided below in Table 2.Table 2: Exemplary cell parameters and how their values may be calculated.

[0066] In a method such as shown in FIG. 1, once the plurality of cell parameter values had been determined 203 for each cell image, those parameter values may be used as a basis for determining 204 a morphology score for each of the images. This morphology score may be a score between 0 and 1 (e.g., in a case where the cell area is represented as a percentage) which indicates a front-facing orientation of the cell relative to the camera when the image of the cell was captured. This score may be calculated by providing the cell parameter values as inputs to a morphology score generation function, which function may calculate the morphology score, MS, for an image using a formula such as set forth below as equation 1:Cell Area x Bounding Box Area x Solidity x Equivalent DiameterEccentricity x Circular ShapeEquation 1 : Exemplary morphology score calculation

[0067] It is also possible that the morphology score, MS, may be calculated by using a formula such as set forth below as equation 2:Equation 2: Exemplary morphology score calculation

[0068] Once the morphology scores had been determined for the plurality of cell images, those morphology scores could be applied in a variety of ways. For example, if the sample containing the blood cells which had been imaged was a patient sample, then the morphology scores maybe used to determine if the patient had a condition which was known to impact blood cell morphology (c.g., sickle cell). Alternatively, if the sample containing the blood cells which had been imaged was a testing sample used to evaluate the performance of the analyzer during a design process, then the morphology scores may be used to test the analyzer’s design. An example of how this may be done is provided below in the context of FIG. 3.

[0069] As shown in FIG. 3, when morphology scores are being applied in the context of a design process, those morphology scores may be used to identify 301 front facing cell images such as by comparing them to a predefined threshold (e.g., a threshold of 0.5 in a case where the morphology scores are on a 0 to 1 scale, a threshold of 800 where the morphology scores are on a scale of 300 to 1200, etc.), and treating the images as front facing cell images or not front facing cell images depending on whether the scores for those images were above or below the threshold. The identifications of front facing images may then be used to obtain 302 an alignment characteristic score for the analyzer. This may be used by calculating the Alignment Characteristic Score, ACS, as a percentage of front facing cells, such as using an equation such as equation 3, below: Front facing cell imagesFront facing cell images + not front facing cell images Equation 3: exemplary equation for front facing cell identification.

[0070] The alignment characteristic score may then be used to determine if the design of the analyzer should be revised. For instance, if the analyzer needed to be able to obtain front facing images of at least 80% of cells, then a comparison 303 could be performed to determine if the alignment characteristic score was above that 80% threshold. If it was, then the design could be treated as suitable, and the design process could end 304. Alternatively, if the alignment characteristic score was not above the threshold, the information determined from analyzing the sample may be presented to a user involved in the analyzer’s design. For example, the parameter values used to calculate the morphology scores could be used to cluster the images in an n-dimensional space where the dimensions corresponded to the cell parameters, and aninterface could be presented 305 which included a plot showing how images were distributed according to the parameters which had the largest impact on the morphology scores (c.g., the two most impactful parameters in a two dimensional plot, or the three most impactful images in a three dimensional plot). An example of such a plot is provided in FIG. 4, which illustrates differences between cells which were classified as front facing and cells which were classified as non-front facing. As can be seen in that figure, the distribution of those cells may vary significantly by parameter, and a user may use this type of difference to indicate that there is an issue with the analyzer design that would require troubleshooting to bring the values of the errant parameter(s) more into line (e.g., the errant parameters may indicate that there is an issue with the sheath that should be enveloping the sample fluid, and the user may focus his or her troubleshooting efforts accordingly).

[0071] While the systems and methods discussed above illustrate potential implementations of the disclosed technology, it should be understood that those systems and methods are intended to be illustrative only, and that there are other systems and methods which could be implemented by those of ordinary skill in the art without undue experimentation based on this disclosure. To illustrate, consider the separation of foreground from background portions of a cell image as discussed above in the context of determining 203 cell parameter values. While it is possible that, in some embodiments where such separation takes place, it may be performed using thresholding as described, other approaches are also possible. For example, in some cases, the foreground (cell) portion of an image may be separated form the background (non-cell) portions of an image using techniques such as described in U.S. patent 10,705,011, issued on 7 / 7 / 2020 for “dynamic focus system and methods,” the disclosure of which is incorporated by reference herein in its entirety. As another example, in some cases algorithms such as the Chan- Vese algorithm, described in T. Chan and L. Vese, “Active contours without edges,” in IEEE transactions on image processing 10(2), 2001, pp. 266-277, the disclosure of which is incorporated herein in its entirety, may be used.

[0072] Other types of variations are also possible. For example, while the discussion of FIG. 3 focused on an alignment characteristic score being used during the design of an analyzer, such acharacteristic may also be used in the analysis of patient samples after design is complete. For example, an analyzer may be configured to determine an alignment characteristic score indicating a percentage of apparently front facing cells in a patient sample and may use such a characteristic to determine if it appeared the patient associated with the sample had a disorder associated with abnormal cell morphology. For instance, if the percentage of cells which appeared to be front facing was below a threshold, then the analyzer may indicate that the patient had or appeared to have a particular disease state, such as sickle cell.

[0073] Similarly, in some cases individual parameters may be used to identify conditions reflected in a patient sample. For instance, in some cases, the morphology score or constituent variables contributing to the morphology score can be used to assess particular cells associated with particular morphology characteristics, such as sickle cells and schistocytes. In some examples, particular solidity value ranges are associated with sickle cells. Other examples, such as using circular shape value ranges to identify schistocytes, or eccentricity value ranges to identify agglutinations are also possible, and will be immediately apparent to those of skill in the art in light of this disclosure. In some examples, the morphology score ranges are correlated to particular cell or disease conditions or the combination of particular variables in the morphology score (e.g., one of more of constituent variables of Equation 1) and the morphology score are used in conjunction to identify particular cell or disease conditions (e.g., sickle cells, or schistocytes). Accordingly, the examples of using the disclosed technology to identify sickle cell, schistocytes, and / or agglutinations should be understood as being illustrative only, and should not be treated as limiting.

[0074] Variations also exist in parameters which may be collected and / or used in some implementations. For example, in some cases, analyzers may be configured to determine additional parameters for a cell image other than the parameters used to generate a morphology score. For instance, it is possible that an analyzer may determine parameters such as ratio of blueness to redness in a cell image or granularity of the nucleus, while determining a morphology score using only the parameters listed in table 1. It is also possible that, in some cases, not all of the parameters listed in table 1 may be used in determining a morphologyscore. For instance, in some cases rather than combining all six of the parameters in table 1 , a morphology score may be determined by using two or more of those parameters, such as placing those parameters in the numerator or denominator of an equation which is similar to Equation 1 or Equation 2 but omits one or more of the parameters that equations include. Still further variations are possible and will be immediately apparent to those of skill in the art in light of this disclosure.

[0075] It is also possible that different embodiments may vary from each other in portions of captured images which are processed to determine parameter values and morphology scores as described above. For example, in some cases all of the images captured by the camera may be processed to determine parameter values, and then each images’ parameter values may be used to calculate a morphology score for that image. However, in other cases, only a subset of the images captured by the camera may be so processed. One example of this type of case was described previously in the context of embodiments which would evaluate whether captured images were in focus and discard images which were not, but this is not the only example. For instance, in some cases only images of certain types of cells, such as images of RBCs, would be processed to determine morphology scores as described above. It is also possible that only a subset of images of a type of cell, such as only a predefined percentage of images of RBCs would be processed and have their morphology scores determined. It is also possible that, in some cases, an analyzer may be configured with separate modes, and that the disclosed technology may only be applied in cases when the analyzer is operating in a specific mode - e.g., a diagnostic mode used to evaluate if the analyzer was appropriately handling cells so as to be able to capture images of the cells when they are front facing. Other variations on subsets, or sub-subsets of images which may be processed as described are also possible, and so the protection provided by this document (or any related document) should not be treated as being limited only to embodiments where all captured images, or where some specific portion of captured images, are processed using the disclosed techniques.

[0076] Accordingly, the examples set forth herein should be understood as illustrative only, and should not be treated as implying limitations on the scope of protection provided by this document or any related document.

[0077] Each of the calculations or operations described herein may be performed using a computer or other processor having hardware, software, and / or firmware. The various method steps may be performed by modules, and the modules may comprise any of a wide variety of digital and / or analog data processing hardware and / or software arranged to perform the method steps described herein. The modules optionally comprising data processing hardware adapted to perform one or more of these steps by having appropriate machine programming code associated therewith, the modules for two or more steps (or portions of two or more steps) being integrated into a single processor board or separated into different processor boards in any of a wide variety of integrated and / or distributed processing architectures. These methods and systems will often employ a tangible media embodying machine-readable code with instructions for performing the method steps described above. Suitable tangible media may comprise a memory (including a volatile memory and / or a non-volatile memory), a storage media (such as a magnetic recording on a floppy disk, a hard disk, a tape, or the like; on an optical memory such as a CD, a CD-R / W, a CD-ROM, a DVD, or the like; or any other digital or analog storage media), or the like.

[0078] All patents, patent publications, patent applications, journal articles, books, technical references, and the like discussed in the instant disclosure are incorporated herein by reference in their entirety for all purposes.

[0079] Different arrangements of the components depicted in the drawings or described above, as well as components and steps not shown or described are possible. For example, in some cases, aspects of processing described herein (e.g., determination of a morphology score, use of such a score to evaluate a patient’s health, use of such a score to assess cell alignment in a flowcell) may be performed in various configurations - for instance, using a processor which is comprised by (or local to) an analyzer, a parallel-processing arrangement, or processing beingperformed remotely from the analyzer which captures the processed images (such as using a cloud based platform, or using a remotely linked computer or system to process the analyzer results). Similarly, some features and sub-combinations are useful and may be employed without reference to other features and sub-combinations. Embodiments of the invention have been described for illustrative and not restrictive purposes, and alternative embodiments will become apparent to readers of this patent. In certain cases, method steps or operations may be performed or executed in differing order, or operations may be added, deleted or modified. It can be appreciated that, in certain aspects of the invention, a single component may be replaced by multiple components, and multiple components may be replaced by a single component, to provide an element or structure or to perform a given function or functions. Except where such substitution would not be operative to practice certain embodiments of the invention, such substitution is considered within the scope of the invention. Accordingly, the claims should not be treated as limited to the examples, drawings, embodiments and illustrations provided above, but instead should be understood as having the scope provided when their terms are given their broadest reasonable interpretation as provided by a general-purpose dictionary, except that when a term or phrase is indicated as having a particular meaning under the heading Explicit Definitions, it should be understood as having that meaning when used in the claims.

[0080] Explicit Definitions

[0081] It should be understood that, in the above examples and the claims, a statement that something is “based on” something else should be understood to mean that it is determined at least in part by the thing that it is indicated as being based on. To indicate that something must be completely determined based on something else, it is described as being “based EXCLUSIVELY on” whatever it must be completely determined by.

[0082] It should be understood that, in the above examples and claims, the term “set” should be understood as one or more things which are grouped together. Similarly, “superset” and “subset” should be each be understood as being synonymous with “set,” with the alternative terms “superset” and “subset” being used only for ease of reading. For the avoidance of doubt,a “superset” of a type of item should not be understood as necessarily referring to all items of that type, unless such is explicitly stated. For example, a reference to capturing a “superset” of cell images should not be understood as requiring that the “superset” of cell images are all of the captured cell images. While the “superset” may be all of the captured cell images, this is not necessary, and should not be treated as being implied by the term “superset.”

Claims

What is claimed is:

1. A cell analysis method comprising: capturing a plurality of cell images, each cell image depicting a respective blood cell of a blood sample as it flows through a flowcell; determining a plurality of cell parameter values for each cell image; and determining a morphology score for each cell image based on the plurality of cell parameter values.

2. The method of claim 1, wherein, for each image from the plurality of cell images, the cell depicted in that cell image is a red blood cell, and wherein the morphology score indicates a front facing orientation of the red blood cell relative to a camera.

3. The method of claims 1 or 2, wherein, for each image from the plurality of cell images, determining the morphology score for that cell image comprises providing the plurality of cell parameter values for that image as input to a morphology score generation function.

4. The method of any one of the preceding claims, wherein, for each image from the plurality of cell images, the plurality of cell parameters values for that cell image comprises two or more of: an area of the cell depicted in that image, a solidity of the cell depicted in that image, an eccentricity of the cell depicted in that image, and an equivalent diameter of the cell depicted in that image.

5. The method of claim 4 wherein, for each image from the plurality of cell images, the plurality of cell parameter values for that cell image comprises: the area of the cell depicted in that image, the solidity of the cell depicted in that image, the eccentricity of a cell depicted in that image, the equivalent diameter of the cell depicted in that image, an area of a bounding box of the cell depicted in that image, and a circular shape of the cell depicted in that image.

6. The method of claim 5 when taken in combination with claim 3, wherein the morphology score generation function comprises a function which is directly proportional to a product of first, and second inputs and is inversely proportional to a product of a third input and a fourth input, wherein, for each image from the plurality of cell images, providing the plurality of cell parameter values for that cell image as input to the morphology score generation function comprises providing the area of the cell depicted in that image as the first input, providing the equivalent diameter of the cell depicted in that image as the second input, providing the eccentricity of the cell depicted in that image as the third input, and providing the circular shape of the cell depicted in that image as the fourth input.

7. The method of any one of the preceding claims, further comprising determining, for each cell image from the plurality of cell images, a plurality of additional parameter values for that cell image, the plurality of additional parameter values being other than the plurality of cell parameter values.

8. The method of any one of the preceding claims, further comprising determining, based on the morphology scores for the plurality of cell images, an alignment characteristic score of an analyzer comprising the flow cell.

9. The method of claim 8, wherein the alignment characteristic score of the analyzer comprises a percentage of front facing cells.

10. The method of any one of the preceding claims, further comprising: presenting a graphical interface comprising a plot displaying a plurality of points, wherein, for each point from the plurality of points, that point corresponds to a cell image from the plurality of cell images, wherein, for each point from the plurality of points, that point is displayed at a location determined based on the plurality of cell parameter values for that cell image.

11. The method of any one of the preceding claims, wherein the method comprises flowing blood cells from the blood sample through the flowcell.

12. The method of claim 1, wherein the plurality of cell images comprises a subset of a larger set of cell images.

13. The method of claim 1, wherein the plurality of cell images comprises a subset of a larger set of cell images, wherein the larger set of cell images are associated with a focus quality, and wherein the plurality of cell images meet at least a particular focus quality threshold.

14. A cell analysis method comprising: capturing a plurality of cell images, each cell image depicting a respective blood cell of a blood sample as it flows through a flowcell; and calculating a score for each cell image; wherein the score represents a front facing orientation assessment.

15. The method of claim 14, wherein, for each image from the plurality of cell images, the cell depicted in that cell image is a red blood cell.

16. The method of any of claims 14 or 15, wherein, for each image from the plurality of cell images, determining the score for that cell image comprises determining a plurality of cell parameter values for that image, and providing the plurality of cell parameter values for that image as input to a morphology score generation function.

17. The method of claim 16, wherein, for each image from the plurality of cell images, the plurality of cell parameters values for that cell image comprises two or more of: an area of the cell depicted in that image, a solidity of the cell depicted in that image, an eccentricity of the cell depicted in that image, and an equivalent diameter of the cell depicted in that image.

18. The method of claim 17 wherein, for each image from the plurality of cell images, the plurality of cell parameter values for that cell image comprises: the area of the cell depicted in that image, the solidity of the cell depicted in that image, the eccentricity of a cell depicted in that image, the equivalent diameter of the cell depicted in that image, an area of a bounding box of the cell depicted in that image, and a circular shape of the cell depicted in that image.

19. The method of claim 18, wherein the morphology score generation function comprises a function which is directly proportional to a product of first, and second inputs and is inversely proportional to a product of a third input and a fourth input, wherein, for each image from the plurality of cell images, providing the plurality of cell parameter values for that cell image as input to the morphology score generation function comprises providing the area of the cell depicted in that image as the first input, providing the equivalent diameter of the cell depicted in that image as the second input, providing the eccentricity of the cell depicted in that image as the third input, and providing the circular shape of the cell depicted in that image as the fourth input.

20. The method of any of claims 16-19, further comprising determining, for each cell image from the plurality of cell images, a plurality of additional parameter values for that cell image, the plurality of additional parameter values being other than the plurality of cell parameter values.

21. The method of any of claims 16-20, further comprising: presenting a graphical interface comprising a plot displaying a plurality of points, wherein, for each point from the plurality of points, that point corresponds to a cell image from the plurality of cell images, wherein, for each point from the plurality of points, that point is displayed at a location determined based on the plurality of cell parameter values for that cell image.

22. The method of any of claims 14-21, further comprising determining, based on the scores for the plurality of cell images, an alignment characteristic score of an analyzer comprising the flow cell.

23. The method of claim 22, wherein the alignment characteristic score of the analyzer comprises a percentage of front facing cells.

24. The method of any of claims 22-23, further comprising: assessing performance of an analyzer which comprises the flowcell based on comparing the alignment characteristic score with an alignment threshold.

25. The method of claim 24 in combination with claim 21, wherein the method further comprises: based on the comparison of the alignment characteristic score with the alignment threshold, presenting the graphical user interface comprising the plot; and updating a design of the analyzer based on the plot.

26. The method of any of claims 14-25, wherein the method comprises flowing blood cells from the blood sample through the flowcell.

27. The method of claim 14, wherein the plurality of cell images comprises a subset of a larger set of cell images.

28. The method of claim 14, wherein the plurality of cell images comprises a subset of a larger set of cell images, wherein the larger set of cell images are associated with a focus quality, and wherein the plurality of cell images meet at least a particular focus quality threshold.

29. A computing device comprising a processor configured to perform the method of any one of claims 1 to 28.

30. A computer program product comprising instructions which, when the program is executed by a computer, cause the computer to carry out the steps of any one of the methods of claims 1 to 2831. A computer-readable storage medium comprising instructions which, when executed by a computer, cause the computer to carry out the steps of any one of the methods of claims 1 to 28.

32. A cell analysis system comprising: a flow cell; a camera; and a computing device ; wherein the cell analysis system is configured to: capture, by using the camera, a plurality of cell images, each cell image depicting a respective blood cell of a blood sample as it flows through the flow cell; determine a plurality of cell parameter values for each cell image; and determine a morphology score for each cell image based on the plurality of cell parameter values.

33. The system of claim 32, wherein, for each image from the plurality of cell images, the cell depicted in that cell image is a red blood cell, and wherein the morphology score indicates a front facing orientation of the red blood cell relative to the camera.

34. The system of claim 32 or 33, wherein the system is further configured to determine, by the computing device and for each image from the plurality of cell images, the morphology score for that cell image at least by providing the plurality of cell parameter values for that image as input to a morphology score generation function.

35. The system of any one of claims 32 to 34, wherein, for each image from the plurality of cell images, the plurality of cell parameters values for that cell image comprises two or more of: an area of the cell depicted in that image, a solidity of the cell depicted in that image, an eccentricity of the cell depicted in that image, and an equivalent diameter of the cell depicted in that image.

36. The system of any one of claims 32 to 35 wherein, for each image from the plurality of cell images, the plurality of cell parameter values for that cell image comprise: the area of the cell depicted in that image, the solidity of the cell depicted in that image, the eccentricity of a cell depicted in that image, the equivalent diameter of the cell depicted in that image, an area of a bounding box of the cell depicted in that image, and a circular shape of the cell depicted in that image.

37. The system of claim 36, wherein the morphology score generation function comprises a function which is directly proportional to a product of first and second inputs and is inversely proportional to product of a third input and a fourth input, wherein, for each image from the plurality of cell images, providing the plurality of cell parameter values for that cell image as input to the morphology score generation function comprises providing the area of the cell depicted in that image as the first input, providing the equivalent diameter of the cell depicted in that image as the second input, providing the eccentricity of the cell depicted in that image as the third input, and providing the circular shape of the cell depicted in that image as the fourth input.

38. The system of any one of claims 32 to 37, wherein the system is further configured to determine, by the computing device and for each cell image from the plurality of cell images, a plurality of additional parameter values for that cell image, the plurality of additional parameter values being other than the plurality of cell parameter values.

39. The system of any one of claims 32 to 38, wherein the system is further configured to obtain, based on the morphology scores for the plurality of cell images, an alignment characteristic score of the cell analysis system.

40. The system of claim 39, wherein the alignment characteristic score of the cell analysis system comprises a percentage of front facing cells.41 . The system of any one of the claims 32 to 40, wherein the system is further configured to present a graphical interface comprising a plot displaying a plurality of points, wherein, for each point from the plurality of points, that point corresponds to a cell image from the plurality of cell images, wherein, for each point from the plurality of points, that point is displayed at a location determined based on the plurality of cell parameter values for that cell image.

42. The system of any of claims 32-41, wherein the cell analysis system comprises flowing the blood cells from the blood sample through the flow cell.

43. The system of claim 32, wherein the plurality of cell images comprises a subset of a larger set of cell images.

44. The system of claim 32, wherein the plurality of cell images comprises a subset of a larger set of cell images, wherein the larger set of cell images are associated with a focus quality, and wherein the plurality of cell images meet at least a particular focus quality threshold.

45. A cell analysis system comprising: a flow cell; a camera; and a computing device ; wherein the cell analysis system is configured to: capture a plurality of cell images, each cell image depicting a respective blood cell of a blood sample as it flows through a flowcell; and calculate a score for each cell image; wherein the score represents a front facing orientation assessment.

46. The system of claim 45, wherein, for each image from the plurality of cell images, the cell depicted in that cell image is a red blood cell.

47. The system of any of claims 45 or 46, wherein, for each image from the plurality of cell images, determining the score for that cell image comprises determining a plurality of cell parameter values for that image, and providing the plurality of cell parameter values for that image as input to a morphology score generation function.

48. The system of claim 47, wherein, for each image from the plurality of cell images, the plurality of cell parameters values for that cell image comprises two or more of: an area of the cell depicted in that image, a solidity of the cell depicted in that image, an eccentricity of the cell depicted in that image, and an equivalent diameter of the cell depicted in that image.

49. The system of claim 48 wherein, for each image from the plurality of cell images, the plurality of cell parameter values for that cell image comprises: the area of the cell depicted in that image, the solidity of the cell depicted in that image, the eccentricity of a cell depicted in that image, the equivalent diameter of the cell depicted in that image, an area of a bounding box of the cell depicted in that image, and a circular shape of the cell depicted in that image.

50. The system of claim 49, wherein the morphology score generation function comprises a function which is directly proportional to a product of first, and second inputs and is inversely proportional to a product of a third input and a fourth input, wherein, for each image from the plurality of cell images, providing the plurality of cell parameter values for that cell image as input to the morphology score generation function comprises providing the area of the cell depicted in that image as the first input, providing the equivalent diameter of the cell depicted in that image as the second input, providing the eccentricity of the cell depicted in that image as the third input, and providing the circular shape of the cell depicted in that image as the fourth input.

51. The system of any of claims 47-50, wherein the cell analysis system is further configured to determine, for each cell image from the plurality of cell images, a plurality of additional parameter values for that cell image, the plurality of additional parameter values being other than the plurality of cell parameter values.

52. The system of any of claims 47-51, wherein the cell analysis system is further configured to: present a graphical interface comprising a plot displaying a plurality of points, wherein, for each point from the plurality of points, that point corresponds to a cell image from the plurality of cell images, wherein, for each point from the plurality of points, that point is displayed at a location determined based on the plurality of cell parameter values for that cell image.

53. The system of any of claims 45-52, wherein the system is further configured to determine, based on the scores for the plurality of cell images, an alignment characteristic score of an analyzer comprising the flow cell.

54. The system of claim 53, wherein the alignment characteristic score of the analyzer comprises a percentage of front facing cells.

55. The system of any of claims 53-54, wherein the cell analysis system is further configured to assess performance of the cell analysis system based on comparing the alignment characteristic score with an alignment threshold.

56. The system of claim 55 in combination with claim 21, wherein the system is further configured to based on the comparison of the alignment characteristic score with the alignment threshold, present the graphical user interface comprising the plot.

57. The method of any of claims 45-46, wherein the system is further configured to flow blood cells from the blood sample through the flowcell.

58. The system of claim 45, wherein the plurality of cell images comprises a subset of a larger set of cell images.

59. The system of claim 45, wherein the plurality of cell images comprises a subset of a larger set of cell images, wherein the larger set of cell images arc associated with a focus quality, and wherein the plurality of cell images meet at least a particular focus quality threshold.

Images