Biofluid analyzer with cell sorting based on optical settings

Abstract

A biofluid analyzer is disclosed. The biofluid analyzer is configured to acquire image data for one or more image planes of an image stack in a prepared biofluid sample. The image data includes first image data associated with a first image plane. Acquiring the first image data includes acquiring first primary image data for the first image plane. The first primary image data is associated with a first incident light setting. The first incident light setting has a first angular light distribution. Acquiring the first image data includes acquiring first secondary image data for the first image plane. The first secondary image data is associated with a second incident light setting. The second incident light setting has a second angular light distribution. The biofluid analyzer is configured to classify cells in the prepared biofluid sample based on the first primary image data and the first secondary image data and provide cellular parameters associated with the cells.

Description

[Technical field]

[0001] The present disclosure relates to the analysis of biological fluid samples, such as blood samples or cell culture samples, and in particular to related tools, methods, and systems with cell sorting based on optical settings. Accordingly, a biological fluid analyzer and related methods, in particular with cell sorting based on optical settings, are provided. [Background technology]

[0002] Currently, analysis of biological fluid samples, for example, determining cell type, classifying cells, etc., can require numerous steps, preparation, resources, and a variety of sophisticated equipment. Blood measurements and other cell-based measurements can rely on distinguishing different cell types, such as distinguishing between different white blood cells (e.g., white blood cell types). The process of identifying cells is largely based on identifying certain characteristics of the cells, for example, the number, size, shape, and / or texture of the nuclear lobes of the cell membrane, the texture and / or area of ​​the cytoplasm.

[0003] It can be difficult to find an optimum between the quality of the optics, e.g., how much detail can be observed with respect to cells, the cost, e.g., better quality is more expensive, and / or the magnification, e.g., better magnification can give more detail but fewer cells can be imaged. This trade-off and the optical specifications can lead to situations where the level of detail in the cell images is insufficient to reliably distinguish cell types from one another. Summary of the Invention [Problem to be solved by the invention]

[0004] Thus, there is a need for biofluid analyzers and related methods, particularly methods of analyzing biofluid samples with improved analysis, cellular differentiation, and accuracy. [Means for solving the problem]

[0005] A biofluid analyzer is disclosed. The biofluid analyzer comprises a memory, an interface, and one or more processors. The biofluid analyzer is configured to acquire image data of one or more image planes of an image stack in a prepared biofluid sample, such as a blood sample or a cell culture sample. The image data includes first image data associated with a first image plane. Acquiring the first image data includes acquiring first primary image data of the first image plane. The first primary image data is associated with a first incident light setting. The first incident light setting can optionally have a first angular light distribution. Acquiring the first image data includes acquiring first secondary image data of the first image plane. The first secondary image data is associated with a second incident light setting. The second incident light setting can optionally have a second angular light distribution. The biofluid analyzer is configured to classify cells in the prepared biofluid sample, such as a blood sample or a cell culture sample, and provide cellular parameters associated with the cells based on the first primary image data and / or the first secondary image data.

[0006] Further disclosed is a method of classifying cells in a prepared biological fluid sample, such as a blood sample or a cell culture sample. The method includes acquiring image data of one or more image planes in an image stack of the prepared biological fluid sample, such as a blood sample or a cell culture sample, the image data including first image data associated with a first image plane. Acquiring the first image data includes acquiring first primary image data of the first image plane. The first primary image data is associated with a first incident light setting having a first angular light distribution. Acquiring the first image data includes acquiring first secondary image data of the first image plane. The first secondary image data is associated with a second incident light setting having a second angular light distribution. The method includes classifying cells in the prepared biological fluid sample, such as a blood sample or a cell culture sample, based on the first primary image data and the first secondary image data. The method includes providing a cellular parameter associated with the cell based on the classification of the cell. The method can be performed using a biofluid analyzer disclosed herein.

[0007] Also disclosed is a system comprising a microscope, an imaging system, an image acquisition device, a biofluid sample cavity for receiving a prepared biofluid sample, such as a blood sample or a cell culture sample, and a biofluid analyzer, the biofluid analyzer being a biofluid analyzer according to the present disclosure. Optionally, the system comprises the imaging system. The imaging system may comprise one or more of a light source assembly configured to emit light, a lens assembly configured to direct light from the light source assembly, and an aperture device configured to apply an aperture to allow light from the lens assembly to pass through the aperture to indirectly define a probe light.

[0008] An advantage of the present disclosure is that improved biological fluid analysis is provided. The present disclosure provides improved cell sorting, such as improved single cell sorting by cell sorting based on light settings. For example, more efficient, accurate, precise, and robust image-based cell parameter determination, such as cell classification and / or single cell classification, such as determining white blood cell WBC, type, and / or cell concentration, such as WBC, platelets, and / or concentration of special pathological cell types, in a biological fluid sample, can be achieved. Furthermore, improved cell classification with higher accuracy is provided, in particular improved cell analysis, such as cell classification, such as analysis and / or classification of white blood cells. By classifying cells based on image data acquired with different incident light settings, such as first primary image data and first secondary image data, the present disclosure provides classification based on more information than known techniques, such as additional cell information. The present disclosure can thereby take into account different incident light settings when classifying cells of a biological fluid sample. In other words, the present disclosure can provide cell classification based on light settings.

[0009] An advantage of the present disclosure is to provide more detailed, efficient, precise, and customized analysis of biological fluid samples. For example, it is possible to determine cellular parameters, such as classification of cells, such as white blood cells, counting of cells, etc., in one or several image planes. An advantage of the present disclosure is to provide improved cell differentiation, such as improved differentiation of white blood cells. Thus, abnormalities in biological fluid samples can be detected more precisely. The present disclosure can also alleviate challenges related to trade-offs between optical system quality, cost, magnification, and / or optical specifications, for example, by providing improved cell differentiation, even with limited instrument quality and / or cost.

[0010] These and other features and advantages of the present invention will become readily apparent to those skilled in the art from the following detailed description of illustrative embodiments of the invention which proceeds with reference to the accompanying drawings. [Brief description of the drawings]

[0011] [Figure 1] FIG. 1 is a block diagram of an exemplary biological fluid analyzer according to the present disclosure. [Diagram 2] FIG. 1 is a schematic diagram of an exemplary system including an exemplary biological fluid analyzer according to the present disclosure. [Diagram 3] FIG. 1 illustrates a schematic diagram of an exemplary system including an exemplary imaging system. [Figure 4] 1 is a flow diagram of an exemplary method according to the present disclosure. [Diagram 5] 1A-1C show exemplary images of cells (e.g., cell regions) acquired at different image planes and different incident light settings in which an exemplary method and / or biofluid analyzer according to the present disclosure is implemented and / or used. [Figure 6] 1A-1C show exemplary images of cells (e.g., cell regions) acquired at different image planes and different incident light settings in which an exemplary method and / or biofluid analyzer according to the present disclosure is implemented and / or used. [Figure 7] 1A-1C show exemplary images of cells (e.g., cell regions) acquired at different image planes and different incident light settings in which an exemplary method and / or biofluid analyzer according to the present disclosure is implemented and / or used. [Figure 8] 1A-1C show exemplary images of cells (e.g., cell regions) acquired at different image planes and different incident light settings in which an exemplary method and / or biofluid analyzer according to the present disclosure is implemented and / or used. [Figure 9] 1A-1C show exemplary images of cells (e.g., cell regions) acquired at different image planes and different incident light settings in which an exemplary method and / or biofluid analyzer according to the present disclosure is implemented and / or used. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0012] Various exemplary embodiments and details are described below with reference to the associated figures. It should be noted that the figures may not be drawn to scale, and that elements of similar structure or function are represented by similar reference numerals throughout the figures. It should also be noted that the figures are intended only to facilitate the description of the embodiments. The figures are not intended to be an exhaustive description of the invention, or a limitation on the scope of the invention. Additionally, the illustrated embodiment need not have all of the aspects or advantages shown. An aspect or advantage described in connection with a particular embodiment is not necessarily limited to that embodiment, and may be implemented in any other embodiment, even if not so shown or explicitly described.

[0013] The figures are schematic and simplified for clarity, showing only those details that aid in the understanding of the present disclosure and omitting other details, and the same reference numbers are used throughout the figures for identical or corresponding parts.

[0014] Hereinafter, whenever the proximal side of the image plane is referred to, the reference refers to the side closest to the camera or sensor (e.g., microscope) or the surface facing the camera or sensor when the image is acquired / captured. Similarly, whenever the distal side of the image plane is referred to, the reference refers to the side furthest from the camera or sensor or the surface facing away from the camera or sensor when the image is acquired / captured. In other words, the proximal side or proximal surface is the side or surface closest to the camera or sensor when the image is acquired / captured, and the distal side is the opposite side or surface with respect to the image plane. In other words, the distal side can be the side or surface closest to the bottom of a container containing a prepared biological fluid sample, such as a blood sample or a cell culture sample, when the image is acquired / captured. A stack of images, such as a stack of image planes, can be considered to include one or more slices of the sample of the image. The optical sensor can be an image sensor having a pixel resolution of, for example, at least 1 megapixel. In one or more exemplary biological fluid analyzers, the camera or optical sensor is configured to be non-replaceable. By non-replaceable, it is meant that the camera or light sensor is at least not directly accessible during normal operation of the biofluid analyzer and cannot be removed without removing parts of the biofluid analyzer that are separate from the camera or light sensor, such as removing a light-blocking panel, etc. This improves the simplicity of the biofluid analyzer and allows for a relatively compact design, as it eliminates the need to provide easy user access to replace the camera or light sensor.

[0015] A biofluid analyzer is disclosed. The biofluid analyzer comprises a memory, an interface, and one or more processors. The biofluid analyzer can comprise an electronic device such as a computer, e.g., a laptop computer or PC, a tablet computer, and / or a mobile phone, e.g., a smartphone. The biofluid analyzer can be, for example, a point-of-care (POC) device. The biofluid analyzer can be, for example, configured to be integrated with a blood gas analyzer. The biofluid analyzer can be, for example, a user device, such as a computer or a mobile phone, configured to perform analysis of a biofluid sample, e.g., a blood sample or a prepared biofluid sample, e.g., a cell culture sample. The biofluid analyzer can be, for example, a part of equipment in a laboratory. The biofluid analyzer can be, for example, used as a near-patient instrument, e.g., a point-of-care device. The biofluid analyzer can be configured to perform blood measurements and / or analysis.

[0016] The biofluid analyzer may be a blood analyzer. In other words, the biofluid analyzer may be configured to analyze human blood and / or animal blood, e.g., mammalian blood. Thus, the prepared biofluid sample may be a prepared blood sample.

[0017] The biofluid analyzer may also be a cell culture analyzer. In other words, the biofluid analyzer may be configured to analyze cell cultures, e.g., cultures of cells derived from multicellular eukaryotic organisms, such as mammalian cells, animal cells, and / or human cells, and / or cultures of cells grown from plant tissue cultures, fungal cultures, and / or microbial cultures (microorganisms). Thus, the prepared biofluid sample may be a prepared cell culture sample.

[0018] In one or more exemplary biofluid analyzers, the biofluid analyzer is a server device, e.g., acts as a server device. In other words, the biofluid analyzer can be considered to be implemented on a server device, e.g., the biofluid analyzer can be configured to run and / or operate on the server device. The biofluid analyzer acting as a server device can be considered to be a device configured to act as a server that communicates with a client device, such as a computer, e.g., a laptop computer or PC, a tablet computer, and / or a mobile phone, e.g., a smartphone. For example, the biofluid analyzer can be a remote server device configured to communicate with the client device. The biofluid analyzer acting as a server device can be configured to perform any one or more of, e.g., acquiring image data, e.g., first image data, acquiring first primary image data, acquiring first secondary image data, classifying cells in a prepared biofluid sample, e.g., a blood sample or a cell culture sample, and providing cellular parameters associated with the cells. The biofluid analyzer acting as a server device can be configured to, for example, output cellular parameters to the client device.

[0019] In one or more exemplary biological fluid analyzers, the biological fluid analyzer is configured to acquire the first secondary image data less than 20 seconds after acquiring the first primary image data, e.g., less than 10 seconds, e.g., less than 5 seconds, e.g., less than 1 second, This allows for relatively rapid acquisition of the first primary image data and the first secondary image data, thereby further improving the likelihood of acquiring the first primary image data and the first secondary image data on at least partially the same prepared biological fluid sample volume.

[0020] In one or more exemplary biological fluid analyzers, the first primary image data is acquired at a first probe volume and the first secondary image data is acquired at a second probe volume, with at least a portion of the first and second probe volumes overlapping. In some examples, at least 90%, at least 70%, or at least 50% of the first and second probe volumes overlap. The biological fluid analyzer can be configured to maintain the probe volume, and possibly the container, stationary, for example, during a change from the first aperture configuration to the second aperture configuration. Additionally or alternatively, this allows imaging of at least a portion of the same volume of the prepared biological fluid by the aperture device in the first aperture configuration and the second aperture configuration. This can be particularly advantageous in combination with an exemplary biological fluid analyzer, where the imaging system is configured to selectively provide a first probe light including light having only incident light angles within a first light angle range, and / or the imaging system is configured to selectively provide a second probe light including light having only incident light angles within a second light angle range, thereby enabling effective acquisition of image data of at least a portion of the same volume of prepared biological fluid sample acquired with different incident light angles.

[0021] In one or more exemplary biofluid analyzers, the biofluid analyzer defines an optical path corresponding to that of a transmission microscope. In other words, the imaging system can be configured to acquire image data from the probe light transmitted through the probe volume. This allows imaging of the prepared bodily fluid sample without relying solely on the fluorescence emitted by the prepared fluid analyzer. Additionally, this can allow for improved simplicity and reduced footprint of the biofluid analyzer, which may be useful, particularly for cell classification, particularly in point-of-care and / or clinical settings. Additionally, it can allow the biofluid analyzer to acquire an image stack of image planes for richer image data and possibly improved cell classification.

[0022] The biofluid analyzer is configured to acquire image data of one or more image planes of an image stack within a prepared biofluid sample, such as a blood sample or a cell culture sample, also represented by an ID. The image data can include one or more images, e.g., a plurality of images, e.g., a stack of images, and / or an image stack. The image data can include a plurality of images of a prepared biofluid sample, such as a blood sample or a cell culture sample, acquired by a microscope and a camera, such as a CMOS image sensor camera. The image data can include, for example, at least 10 images, at least 20 images, at least 30 images, at least 40 images, at least 50 images, or at least 100 images. An image of the image data can have an area according to the area of ​​the camera image sensor and the microscope magnification, e.g., A=A_im / M 2where A is the area of ​​the captured image, M is the microscope magnification of the microscope, and A_im is the area of ​​the camera image sensor. The images of the image data can have a pixel size in the range of 0.1 μm to 5 μm, e.g., 0.5 μm, 1 μm, or 2 μm, depending on the resolution of the camera and objective lens used. The image data can include a plurality of images of a prepared biological fluid sample, such as a blood sample or a cell culture sample, where each image of the plurality of images is associated with an image plane of the prepared biological fluid sample, such as a blood sample or a cell culture sample. The microscope and camera can acquire / acquire the multiple images by stepping an optical focal plane along a z-axis, such as a vertical direction of the prepared biological fluid sample, such as a blood sample or a cell culture sample. The image data can thus include a plurality of images associated with an image plane, where each image plane is separated from the next acquired / acquired image plane and / or the previously acquired / acquired image plane by a distance Δz. Δz can be a step increment per acquired / acquired image. For example, the image data may include multiple images of a prepared biological fluid sample, such as a blood sample or a cell culture sample, where each image may be associated with an image plane that is equidistant from the next acquired / acquired image plane and / or the previously acquired / acquired image plane. In other words, the image data may include a 3D image stack, such as an image stack, where each image of the image stack is associated with an image plane that has a different associated height along the z-axis of the prepared biological fluid sample, such as a blood sample or a cell culture sample. In other words, each image plane may be associated with a unique height within the prepared biological fluid sample, such as a blood sample or a cell culture sample, contained within a container, e.g., a blood sample or a cell culture sample contained within a cuvette. The distance between two image planes may be referred to as the inter-image distance. The distance between two image planes may vary, for example, depending on the type of cells of interest. For example, the distance between two image planes may also vary depending on the numerical aperture NA of the microscope used, and therefore the depth of field DOF.This can be done to achieve an image in the image stack where the cell has the best focus. For example, when the cell of interest is a platelet, the distance between the two image planes can be, for example, in the range of 1 μm to 10 μm, for example, in the range of 2 μm to 8 μm, for example, in the range of 3 μm to 6 μm, for example, in the range of 1 μm to 8 μm, and / or in the range of 1 μm to 6 μm. For example, when the cell of interest is a platelet, the distance between the two image planes can be, for example, 5.04 μm, for example, because the platelet has a diameter in the range of 1 μm to 5 μm, for example, in the range of 2 μm to 3 μm. For example, when the cell of interest is a white blood cell, the distance between the two image planes can be, for example, in the range of 4 μm to 15 μm, for example, in the range of 5 μm to 12 μm, for example, in the range of 5 μm to 10 μm, for example, in the range of 8 μm to 12 μm, and / or in the range of 9 μm to 11 μm. For example, the distance between the two image planes can be, for example, 4 μm, 5 μm, 6 μm, 7 μm, 8 μm, 9 μm, 10 μm, and / or 11 μm. A white blood cell having a diameter, for example, in the range of 5 μm to 10 μm, can, for example, belong to two image planes. The image data can include multiple images of a central portion of the biological fluid sample (such as a blood sample or a prepared biological fluid sample, such as a cell culture sample). In other words, the image data can include multiple acquired / acquired images of the prepared biological fluid sample, such as a blood sample or a cell culture sample, that represent an area or volume of the prepared biological fluid sample, such as a blood sample or a cell culture sample, that is located away from an edge of a container in which the prepared biological fluid sample, such as a blood sample or a cell culture sample, is contained. An advantage of having an image of the prepared biological fluid sample that represents an area or volume of the prepared biological fluid sample that is located away from an edge of a container in which the prepared biological fluid sample is contained is that one can avoid seeing the edge of the glass of the container, such as a stain on the glass of the container. In one or more exemplary biological fluid analyzers, the image data, such as one or more images of the image data, can be cropped.For example, an image obtained at a resolution of 20 megapixels can be cropped by cropping 20% ​​of the side length of the field of view FOV. The cropped image can be of a central portion of the biological fluid sample, such as a reduced portion of the FOV. The central portion of the biological fluid sample can have the best optical resolution and the least optical aberrations. An advantage of using cropped images is that a larger number of images can be selected from the image data. For example, substantially all images of the image data can be selected, such as at least 20 images, at least 30 images, or at least 40 images. By selecting more images, it is possible to compensate for non-precise distance movements (such as mechanical delta Z movements) in the focus mechanism of the microscope. An additional advantage of using cropped images is that computing resources for characterizing images, such as determining cell region populations, are saved.

[0023] Each image of the image data ID can include multiple representations. The multiple representations can include multiple particles, e.g., cells, e.g., white blood cells WBC, platelets PLT, red blood cells RBC, blood component clots, cellular debris, and / or foreign particles, e.g., dust, sediment, or residue from the container, etc. The multiple representations can include multiple cells, e.g., mature cells, e.g., reticulocytes (slightly immature RBCs), lymphocytes, and / or monocytes, segmented and banded granulocytes (slightly immature neutrophils), neutrophils, eosinophils, and / or basophils, and immature cells, e.g., normoblasts, erythroblasts, proerythroblasts, metamyelocytes, myelocytes, promyelocytes, myeloblasts, monoblasts, and / or lymphoblasts.

[0024] The prepared biological fluid sample may include a biological fluid sample that has been prepared by one or more reagents, chemicals, treatments, and / or processes. The prepared biological fluid sample may include, for example, a stained, e.g., chemically stained, biological fluid sample. The prepared biological fluid sample may include, for example, a biological fluid sample that has been positioned / fixed such that substantially no cell movement occurs during acquisition / acquisition of an image of the prepared biological fluid sample. The prepared biological fluid sample may be understood to be a solution that includes a biological fluid and one or more reagents and / or chemicals. The prepared biological fluid sample may be understood to be a lysate, e.g., a lysed biological fluid sample. The prepared biological fluid sample may be placed / positioned in a container, such as a cuvette, while image data, e.g., multiple images of the prepared biological fluid sample, are acquired / acquired. The height with which the image plane is associated may be a height or distance, e.g., on the z-axis, relative to the bottom of the container / cuvette or the camera / microscope when the image is acquired / captured. The image plane, in which the multiple images may extend, may be a two-dimensional plane, e.g., an xy plane orthogonal to the z-axis.

[0025] In an exemplary biofluid analyzer where the cuvette is a multi-purpose cuvette, the biofluid analyzer can be configured as a multi-purpose device with the necessary fluidic systems and mechanisms to perform multiple measurements. Such an exemplary biofluid analyzer can further comprise a solution pack containing one or more solutions, and the exemplary biofluid analyzer can be configured to manage and release the solution pack to at least partially automatically prepare a prepared bodily fluid sample after aspiration by using its fluidic systems and mechanisms, and can be configured to control the fluidic systems to execute a cleaning program for at least the multi-purpose cuvette before and / or after the biofluid analysis.

[0026] The prepared biological fluid sample may be a prepared blood sample that has been prepared by one or more reagents, chemicals, treatments, and / or processes. The prepared blood sample may include, for example, a stained, e.g., chemically stained, blood sample. The prepared blood sample may include, for example, a hemolyzed blood sample, e.g., most of the red blood cells in the blood sample have been removed. The prepared blood sample may include, for example, a blood sample that has been positioned / fixed such that substantially no cell movement occurs during acquisition / acquisition of an image of the prepared blood sample. The prepared blood sample may be understood to be a solution that includes blood and one or more reagents and / or chemicals. The prepared blood sample may be understood to be a lysate, e.g., a lysed blood sample. The prepared blood sample may be placed / positioned in a container, such as a cuvette, during which image data, e.g., multiple images of the prepared blood sample, are acquired / acquired. The height with which the image plane is associated may be a height or distance, e.g., on the z-axis, relative to the bottom of the container / cuvette or the camera / microscope when the image is acquired / captured. The image plane, in which the multiple images may extend, may be a two-dimensional plane, e.g., an xy plane orthogonal to the z-axis.

[0027] Optionally, the image data ID includes image data including a first image data ID_1, e.g., a first image data ID_1 associated with a first image plane also represented by IP_1. In other words, the image data may include a first image data including one or more images associated with a first image plane of an image stack. Optionally, the biological fluid analyzer is configured to select an image also represented by I_i, where i is the number of selected images associated with an image plane also represented by IP_i of a prepared biological fluid sample from the image data ID, e.g., an image stack. The biological fluid analyzer may be configured to select a first image also represented by I_1 associated with a first image plane IP_1 of a prepared biological fluid sample from the image data ID. In other words, selecting the image I_i may include selecting a first image I_1 associated with a first image plane IP_1 of a prepared biological fluid sample from the image data ID. The first image I_1 may be selected from a plurality of images acquired from the image data ID. Optionally, the biofluid analyzer can be configured to select the second image I_2, the third image I_3, the fourth image I_4, and / or the fifth image I_5. In one or more exemplary biofluid analyzers, the biofluid analyzer can be configured to select more images, e.g., 10 images, 20 images, or more. The images selected from the image data can be selected from a set of images, e.g., at least 20 images, each image associated with an image plane of the prepared biofluid sample. Selecting the image data can include storing one or more images of the prepared biofluid sample in a dataset, e.g., to provide a stack of images. Each image of the stack of images can be associated with an image plane having a different associated height along a z-axis of the prepared biofluid sample. In other words, each image plane can be associated with a different height along a z-axis of the prepared biofluid sample.

[0028] Acquiring the first image data ID_1 includes acquiring first primary image data ID_1_1 of a first image plane IP_1. The first primary image data ID_1_1 is associated with a first incident light setting ILS_1. Optionally, the first incident light setting ILS_1 may have a first angular light distribution ALD_1. In other words, the first primary image data ID_1_1 may be considered as having been acquired by and / or according to the first incident light setting ILS_1 having the first angular light distribution ALD_1. The first primary image data ID_1_1 may be associated with image data acquired from the prepared biological fluid sample while the prepared biological fluid sample is illuminated by the probe light according to the first incident light setting ILS_1 having the first angular light distribution ALD_1.

[0029] The first incident light setting ILS_1 can include one or more microscope settings, one or more light source settings, and / or one or more aperture settings. The first incident light setting can provide a probe light for interrogating the prepared biological fluid sample, such as light incident on the prepared biological fluid sample. In other words, the first incident light setting ILS_1 can be provided via one or more of a microscope setting, a light source setting, and / or an aperture setting.

[0030] In one or more exemplary biological fluid analyzers, the first image plane is associated with a first height, also represented as H_1, within the prepared biological fluid sample. The first height to which the first image plane is associated can be a height, for example on the z-axis, relative to the bottom of the container / cuvette or camera / microscope when the first image data, such as the first image, is acquired / captured.

[0031] Acquiring the first image data ID_1 includes acquiring first secondary image data ID_1_2 of the first image plane IP_1. The first secondary image data ID_1_2 is associated with a second incident light setting ILS_2. Optionally, the second incident light setting ILS_2 can have a second angular light distribution ALD_2. In other words, the first secondary image data ID_1_2 can be considered as having been acquired by and / or according to the second incident light setting ILS_2 having the second angular light distribution ALD_2. The first secondary image data ID_1_2 can be associated with image data acquired from the prepared biological fluid sample while the prepared biological fluid sample is illuminated by the probe light according to the second incident light setting ILS_2 having the second angular light distribution ALD_2.

[0032] The second incident light setting ILS_2 can include one or more microscope settings, one or more light source settings, and / or one or more aperture settings. The second incident light setting can provide a probe light for interrogating the prepared biological fluid sample, such as light incident on the prepared biological fluid sample. In other words, the second incident light setting ILS_2 can be provided via one or more of a microscope setting, a light source setting, and / or an aperture setting.

[0033] The angular light distribution can be considered as the spatial distribution of light of the incident light. In other words, the angular light distribution can be considered as the spatial distribution of one or more light components incident on the prepared biological fluid sample. For example, the angular light distribution can indicate one or more light angles (e.g., spatial light angles) of the probe light interrogating the prepared biological fluid sample.

[0034] In the one or more exemplary biological fluid analyzers, acquiring the first image data includes acquiring first tertiary image data ID_1_3 of a first image plane IP_1. In the one or more exemplary biological fluid analyzers, the first tertiary image data ID_1_3 is associated with a third incident light setting ILS_3 having a third angular light distribution ALD_3. In the one or more exemplary biological fluid analyzers, classifying the cells in the prepared biological fluid sample is based on the first tertiary image data ID_1_3.

[0035] In other words, the first tertiary image data ID_1_3 can be considered to have been acquired by and / or according to a third incident light setting ILS_3 having a third angular light distribution ALD_3. The first tertiary image data ID_1_3 can be associated with image data acquired from the prepared biological fluid sample while the prepared biological fluid sample is illuminated by the probe light according to the third incident light setting ILS_3 having the third angular light distribution ALD_3.

[0036] The third incident light setting ILS_3 can include one or more microscope settings, one or more light source settings, and / or one or more aperture settings. The third incident light setting can provide a probe light for interrogating the prepared biological fluid sample, such as light incident on the prepared biological fluid sample. In other words, the third incident light setting ILS_3 can be provided via one or more of a microscope setting, a light source setting, and / or an aperture setting.

[0037] The description regarding the first incident light setting ILS_1 and / or the second incident light setting ILS_2 may also be applied to the third incident light setting ILS_3. In one or more exemplary biological fluid analyzers, the first incident light setting includes one or more of a lens assembly setting, an aperture setting, an angle setting, and a light source setting. In other words, the first incident light setting ILS_1 can include one or more of a first lens assembly setting, a first aperture setting, a first angle setting, and a first light source setting.

[0038] In one or more exemplary biological fluid analyzers, the second incident light setting ILS_2 includes one or more of a lens assembly setting, an aperture setting, an angle setting, and a light source setting. In other words, the second incident light setting ILS_2 can include one or more of a second lens assembly setting, a second aperture setting, a second angle setting, and a second light source setting.

[0039] A lens assembly setting can be considered as one or more settings associated with one or more lens assemblies, e.g., one or more of a first lens assembly, a second lens assembly, and a third lens assembly, which can be formed from one or more lenses. A lens assembly setting can be considered as one or more lenses of a lens assembly, which can be arranged to direct light received from a light source according to a desired light pattern. For example, the lens assembly can be configured to direct the received light toward a back focal point of the lens assembly. A lens assembly setting can include a configuration of the lens assembly to achieve a desired light pattern, such as a first incident light setting ILS_1 and / or a second incident light setting ILS_2. This can be achieved by a suitable combination of lenses, such as convex and concave lenses with suitable refractive indices.

[0040] The aperture setting may be considered as one or more settings associated with one or more aperture assemblies, e.g., one or more of a first aperture assembly, a second aperture assembly, and a third aperture assembly, which may be formed from one or more apertures. The aperture setting may include aperture sizes, e.g., aperture sizes of 2 mm, 3 mm, 4 mm, and / or 5 mm. The aperture size may be in the range of 1 mm to 10 mm. The aperture setting may be configured to provide a first incident light setting ILS_1 and / or a second incident light setting ILS_2. In other words, the aperture setting may define a set of settings for one or more aperture assemblies, e.g., an aperture assembly of a microscope, to provide a first incident light setting ILS_1 and / or a second incident light setting ILS_2. The aperture setting may include a configuration of the aperture assembly to achieve a desired light pattern, e.g., a first incident light setting ILS_1 and / or a second incident light setting ILS_2.

[0041] A light source setting can be considered as one or more settings associated with one or more light sources, for example, one or more incident light source assemblies. The light source assembly can be formed from a support element supporting multiple light sources, such as multiple LEDs. The light source assembly can include an electrical circuit for providing power to the multiple light sources. The light source assembly can be configured to emit light according to one or more configurations, such as one or more light configurations. The light source setting can be configured to provide a first incident light setting ILS_1 and / or a second incident light setting ILS_2. In other words, the light source setting can define a set of settings for one or more light source assemblies, such as a light source assembly of a microscope, for example, to provide a first incident light setting ILS_1 and / or a second incident light setting ILS_2. The light source setting can include a configuration of the light source assembly to achieve a desired light pattern, such as a first incident light setting ILS_1 and / or a second incident light setting ILS_2. The light source setting, such as a first incident light setting ILS_1 and / or a second incident light setting ILS_2, can include a pattern setting and / or an intensity setting.

[0042] An angle setting can be considered as one or more settings associated with one or more angles, e.g., one or more incident light angles of the probe light. An angle setting can refer to an angle of incident light in the range of 0°-90°. For example, a particular cell feature, such as cell size and / or cell shape, can be observed at a larger angle, e.g., a larger aperture (e.g., a larger numerical aperture). An angle setting can be provided via one or more of a lens assembly setting, an aperture setting, and a light source setting. An angle setting can be configured to provide a first incident light setting ILS_1 and / or a second incident light setting ILS_2, e.g., a first light angle LA_1 and / or a second light angle LA_2. In other words, an angle setting can define a set of settings for one or more lens assemblies, aperture assemblies, and / or light source assemblies, e.g., to provide a first incident light setting ILS_1 and / or a second incident light setting ILS_2. The angular settings may include one or more configurations of lens assembly settings, aperture settings, and light source settings to achieve a desired angular pattern, such as a first incident light setting ILS_1 and / or a second incident light setting ILS_2.

[0043] In one or more exemplary biological fluid analyzers, the first incident light setting ILS_1 is configured to provide an incident light, also represented as a first probe light, including a first light component LC_1, with a first light angle LA_1 greater than a first angle A_1. The first light component LC_1 may include one or more light rays included within the first light angle LA_1. The first light component LC_1 may include a first light range. In other words, the first primary image data ID_1_1 may be associated with the first light component LC_1 and / or the first light angle LA_1. The first primary image data ID_1_1 may be acquired from a prepared biological fluid sample, for example, a first image plane IP_1 is illuminated by the first probe light and / or the first light component LC_1, for example, at the first light angle LA_1. By greater than the first angle A_1, it may be understood that the first light component LC_1 has a first light angle LA_1 of at least the first angle A_1. The first angle A_1 can be considered as a first incident light angle of the light impinging on the prepared biological fluid sample. The first angle A_1 can be in the range of 0° to 60°, e.g., the first light component is greater than the first angle A_1 in the range of 0° to 30°. The first angle A_1 can be measured relative to an optical axis of the imaging device, e.g., an axis perpendicular to the prepared biological fluid sample, e.g., can be measured about an axis perpendicular to the prepared biological fluid sample.

[0044] The first probe light provided according to the first incident light setting ILS_1 can include light having an incident light angle with respect to an optical axis of the imaging system less than a first top light angle. The first top light angle can be 15 degrees or less, such as 15 degrees or less than 10 degrees, or even 5 degrees or less. In one or more exemplary biological fluid analyzers, the first probe light provided according to the first incident light setting ILS_1 does not have, or is substantially free of, light having an incident light angle with respect to the optical axis greater than the first top light angle.

[0045] In one or more exemplary biological fluid analyzers, the first probe light provided according to the first incident light setting ILS_1 can include light having an incident light angle relative to the optical axis greater than a first bottom light angle. The first bottom light angle can be 0 degrees or greater, e.g., 2 degrees or greater, 5 degrees or greater, or 10 degrees or greater. In one or more exemplary biological fluid analyzers, the first probe light provided according to the first incident light setting ILS_1 has no, or substantially no, light having an incident light angle relative to the optical axis less than the first bottom light angle.

[0046] The first lower light angle and the first upper light angle can define a first light angle range of the first probe light. The first light angle range can be 0 degrees to 15 degrees, such as 0 degrees to 10 degrees, such as 5 degrees to 10 degrees.

[0047] In other words, the imaging system can be configured to selectively provide a first probe light that includes light having only incident light angles within a first light angle range. In one or more exemplary biological fluid analyzers, the second incident light setting ILS_2 is configured to provide an incident light, also represented as a second probe light, including a second light component LC_2, with a second light angle LA_2 greater than the second angle A_2. The second light component LC_2 can include one or more light rays included within the second light angle LA_2. The second light component LC_2 can include a second light range. In other words, the first secondary image data ID_1_2 can be associated with the second light component LC_2 and / or the second light angle LA_2. The first secondary image data ID_1_2 can be acquired from a prepared biological fluid sample, for example, where a second image plane IP_2 is illuminated by the second probe light and / or the second light component LC_2, for example, at the second light angle LA_2. By greater than the second angle A_2, it can be understood that the second light component LC_2 has a second light angle LA_2 of at least the second angle A_2. The second angle A_2 can be considered as a second incident light angle of the light impinging on the prepared biological fluid sample. The second angle A_2 can be in the range of 10° to 90°, e.g., the second light component is greater than the second angle A_2 in the range of 15° to 90°. The second angle A_2 can be measured relative to an optical axis of the imaging device, e.g., an axis perpendicular to the prepared biological fluid sample, e.g., about an axis perpendicular to the prepared biological fluid sample.

[0048] The second probe light provided according to the second incident light setting ILS_2 can include light having an incident light angle with respect to an optical axis of the imaging system that is less than the second top light angle. The second top light angle can be 30 degrees or less, such as 20 degrees or less, or even 17 degrees or less. In one or more exemplary biological fluid analyzers, the second probe light provided according to the second incident light setting ILS_2 does not have, or is substantially free of, light having an incident light angle with respect to the optical axis that is greater than the second top light angle.

[0049] In one or more exemplary biological fluid analyzers, the second probe light provided according to the second incident light setting ILS_2 can include light having an incident light angle relative to the optical axis greater than the second bottom light angle. The second bottom light angle can be greater than the first top light angle. The second bottom light angle can be 0 degrees or greater, e.g., 2 degrees or greater, 5 degrees or greater, or 10 degrees or greater. In one or more exemplary biological fluid analyzers, the second probe light provided according to the second incident light setting ILS_2 has no, or is substantially no, light having an incident light angle relative to the optical axis less than the second bottom light angle.

[0050] The second lower light angle and the second upper light angle can define a second light angle range of the second probe light. The second light angle range can be 0 degrees to 20 degrees, such as 5 degrees to 20 degrees, such as 10 degrees to 17 degrees. The second light angle range and the first light angle range can be non-overlapping.

[0051] In other words, the imaging system can be configured to selectively provide a second probe light that includes light having only incident light angles within the second light angle range. In one or more exemplary biological fluid analyzers, N incident light settings ILS_1, ILS, ILS_2, ..., ILS_N may be applied to each image plane. The number N of incident light settings may be 2, 3, 4, 5, 6, 7, or more. N may be in the range of 2 to 5.

[0052] In one or more exemplary biological fluid analyzers, the first incident light setting ILS_1 includes an aperture setting indicative of a first aperture size AS_1 used to acquire the first primary image data. The first aperture size AS_1 can be 2 mm, 3 mm, 4 mm, and / or 5 mm. The first aperture size AS_1 can be in a range of 1 mm to 10 mm. In other words, the first primary image data ID_1_1 can be acquired when using the first aperture size AS_1 to illuminate the prepared biological fluid sample. In other words, the first incident light setting ILS_1 can be acquired by a first aperture AP_1.

[0053] In one or more exemplary biological fluid analyzers, the second incident light setting ILS_2 includes an aperture setting indicative of a second aperture size AS_2 used to acquire the second primary image data. The second aperture size AS_2 can be 2 mm, 3 mm, 4 mm, and / or 5 mm. The second aperture size AS_2 can be in a range of 1 mm to 10 mm. In other words, the first secondary image data ID_1_2 can be acquired when using the second aperture size AS_1 to illuminate the prepared biological fluid sample. The aperture size can be considered as an opening size of the aperture assembly. In other words, the second incident light setting ILS_2 can be acquired by the second aperture AP_2.

[0054] In one or more exemplary biological fluid analyzers, the second incident light setting ILS_2 is configured to provide an incident light angle that does not overlap with the incident light angle by the first incident light setting ILS_1. In other words, the second incident light setting ILS_2 can be configured to provide an incident light beam that does not overlap with the incident light beam by the first incident light setting ILS_1. For example, the first light component LC_1 can be non-overlapping with the second light component LC_2. In other words, the second incident light setting ILS_2 can be configured to provide a spatial distribution of light components that does not overlap with the spatial distribution of light components provided by the first incident light setting ILS_1.

[0055] In one or more exemplary biofluid analyzers, the first aperture size is different from the second aperture size. For example, the first aperture size can be 3 mm and the second aperture size can be 4 mm. In one or more exemplary biofluid analyzers, the shape of the first aperture AP_1 is different from the shape of the second aperture AP_2. The first aperture can have a first shape that is circular, elliptical, rectangular, square, non-circular, or any other suitable shape. The second aperture can have a second shape that is circular, elliptical, rectangular, square, non-circular, or any other suitable shape.

[0056] In one or more exemplary biological fluid analyzers, the first incident light setting ILS_1 and / or the second incident light setting ILS_2 include an aperture setting exhibiting a numerical aperture in the range of 0 to 0.7.

[0057] In one or more exemplary biofluid analyzers, the biofluid analyzer is configured to determine a set of cell regions, also represented by SCR_i, based on the image data ID. In one or more exemplary biofluid analyzers, the biofluid analyzer is configured to determine a set of cell regions, also represented by SCR, based on the first image data ID_1.

[0058] In one or more exemplary biofluid analyzers, determining the cell region set SCR_i may include characterizing image data, e.g., characterizing one or more images I_i of an image stack. In one or more exemplary biofluid analyzers, determining the cell region set SCR_i based on the image data includes determining a cell region set, also denoted SCR_i, belonging to an image plane IP_i. A cell region, also denoted CR_k_i, k=1,2,...K, may be understood to be a group of pixels in an image I_i representing one or more cells, a part of a cell, several parts of a cell, or an optical phenomenon related to one or more cells, where K is the number of cell regions in the cell region set SCR_i and i is the number of selected images. In other words, the cell region set SCR_i includes one or more cell regions CR_k_i, e.g., one or more groups of pixels in an image I_i representing one or more cells, one or more parts of one or more cells, or an optical phenomenon related to one or more cells. The cell region may preferably represent a single cell, a portion of a single cell, or an optical phenomenon associated with a single cell.

[0059] In one or more exemplary biofluid analyzers, determining the cell region set SCR_i may include characterizing a first image I_1. In one or more exemplary biofluid analyzers, determining the cell region set is based on first image data ID_1. In one or more exemplary biofluid analyzers, determining the cell region set SCR based on the image data includes determining a first cell region set SCR_1 belonging to a first image plane IP_1. In other words, characterizing the image I_i may include characterizing the first image I_1. In other words, determining the cell region set SCR_i based on the image data may include determining a cell region set SCR_i belonging to an image plane IP_i. In other words, determining the cell region set SCR_i belonging to an image plane IP_i may include determining a first cell region set SCR_1 in focus in or associated with the first image plane IP_1. Belonging to an image plane, e.g., being in focus, can be considered to be best focused on the cell regions CR_k_i (e.g., cells) of the first cell region set SCR_1. In other words, the cell region set SCR_i, e.g., the first cell region set SCR_1, can include one or more cell regions, e.g., one or more groups of pixels in the first image I_1 representing one or more cells, one or more parts of one or more cells, or optical phenomena related to one or more cells. In one or more exemplary biofluid analyzers, determining the cell region set SCR_i can include storing in a dataset one or more cell regions CR_k_i associated with a group of pixels in the image I_i representing one or more cells, parts of cells, parts of cells, or optical phenomena related to one or more cells.

[0060] Belonging to an image plane IP_i, for example belonging to the first image plane IP_1, can be understood to mean that the cell region CR_k_i represents a cell (for example, a volume of a cell) that is mostly located in the image plane IP_i or that is assigned to or in focus in the image plane at the time the image is acquired / captured. For example, belonging to an image plane IP_i can be understood to mean that the cell region CR_k_i represents a cell that is mostly located in a volume around the image plane IP_i, for example, that is centered around the image plane IP_i. For example, belonging to an image plane IP_i can be understood to mean that the cell region CR_k_i represents a cell that is located in a volume around the image plane IP_i. For example, when the cell of interest is a platelet, for example since the platelet has a diameter in the range of 1 μm to 5 μm, for example in the range of 2 μm to 3 μm, the distance between two image planes, for example a first distal distance, also represented as DD_1, between the first image plane IP_1 and a first distal image plane DIP_1 distal to the first image plane IP_1, and a first proximal distance, also represented as PD_1, between the first image plane IP_1 and a first proximal image plane PIP_1 proximal to the first image plane IP_1, can be in the range of 1 μm to 20 μm, for example in the range of 2 μm to 15 μm, for example in the range of 2 μm to 10 μm, for example in the range of 2 μm to 8 μm, in the range of 3 μm to 6 μm, in the range of 1 μm to 8 μm, and / or in the range of 1 μm to 6 μm. For example, when the cells of interest are platelets, e.g., because platelets have a diameter in the range of 1 μm to 5 μm, the first distal distance DD_1 between the first image plane IP_1 and the first distal image plane DIP_1, and the first proximal distance, also represented by PD_1 between the first image plane IP_1 and the first proximal image plane PIP_1, can be, e.g., 3 μm, 3.5 μm, 4 μm, 4.5 μm, 5 μm, 5.5 μm, 6 μm, 7 μm, 8 μm, 9 μm, and / or 10 μm. For example, the distance D between the image plane IP_i and the next neighboring image plane is 5.04 μm, and belonging to the image plane IP_i can be understood to mean that the cell region CR_k_i represents a cell located within a volume of +2.02 μm and -2.02 μm around the image plane IP_i.Belonging to an image plane IP_i can be understood as a cell region CR_k_i representing a cell located within a volume between the image plane IP_i and a next neighboring image plane, e.g., the next neighboring distal image plane and / or the next neighboring proximal image plane. In one or more exemplary biofluid analyzers, a cell region set can extend into two or more image planes. For example, when a cell region represents a cell that is larger than the distance between two image planes, the cell can extend into two or more image planes. In one or more exemplary biofluid analyzers, a cell region set can be assigned to an image plane, e.g., the first image plane IP_1. In one or more exemplary biofluid analyzers, a distance between two neighboring image planes can be based on a size of a cell type of interest, e.g., to ensure that a cell belongs to an image plane. For example, a white blood cell having a diameter in the range of 5 μm to 10 μm can belong to two image planes, for example.

[0061] The biofluid analyzer is configured to classify cells, also represented by CE, in the prepared biofluid sample based on the first primary image data ID_1_1 and / or the first secondary image data ID_1_2. In other words, the biofluid analyzer can be configured to characterize the cellular content of the prepared biofluid sample based on the first primary image data ID_1_1 and / or the first secondary image data ID_1_2. For example, the biofluid analyzer can be configured to classify each cell of the cellular region of the first image plane IP_1 based on the first primary image data ID_1_1 and / or the first secondary image data ID_1_2. In one or more exemplary biofluid analyzers, the biofluid analyzer can be configured to classify a cell, e.g., a first cell CE_1 in a first cellular region CR_k_1 of a cellular region set SCR_i, based on the first primary image data ID_1_1 and / or the first secondary image data ID_1_2. Classifying the first cell CE_1 in the first cell region CR_k_1 of the cell region set SCR_i may include characterizing the cellular content of the prepared biological fluid sample based on the first primary image data and / or the first secondary image data ID_1_2.

[0062] In one or more exemplary biological fluid analyzers, the biological fluid analyzer is configured to classify each cell in a cellular region of a first image plane IP_1 based on the first primary image data ID_1_1 and / or the first secondary image data ID_1_2.

[0063] The biological fluid analyzer is configured to classify cells CE in the prepared biological fluid sample based on the first primary image data and / or the first secondary image data ID_1_2, and provide cellular parameters, also represented by CPs, associated with the cells CE.

[0064] In other words, the biofluid analyzer is configured to provide a cell parameter CP_k_i indicative of a cell type of the cell CE based on the classification of the cell CE. The biofluid analyzer can be configured to determine a cell parameter, also represented by CP_k_i, for each cell region CR_k_i of the cell region set SCR_i, where i is the number of selected images, e.g., image planes, and k is the number of cell regions. Providing the cell parameter CP_k_i can include classifying the cell CE associated with or represented by the cell region CR_k_i based on the first primary image data ID_1_1 and / or the first secondary image data ID_1_2. The cell parameter CP_k_i can include, for example, cell type information. The cell parameter CP_k_i can include, for example, a cell type identifier indicative of a cell type of the cell. Providing the cell parameter CP_k_i can include determining the cell parameter CP_k_i based on the classification. Providing the cell parameters CP_k_i may include classifying the cell CE associated with or represented by the cell region CR_k_i based on optical phenomena (such as optical phenomena of cells in neighboring image planes and / or at different incident light settings), e.g. optical phenomena detected in the first primary image data ID_1_1 and / or the first secondary image data ID_1_2. Providing the cell parameters CP_k_i may include classifying the cell CE associated with or represented by the cell region CR_k_i based on information determined from the first primary image data ID_1_1 and / or the first secondary image data ID_1_2.

[0065] The biofluid analyzer may be configured to classify a first cell CE_1 in a first cell region CR_1 of a cell region set SCR_i based on the first primary image data and / or the first secondary image data ID_1_2 to provide a first cell parameter, also denoted as CP_1, indicative of a cell type of the first cell CE_1. The cell parameter may include the first cell parameter. In other words, the biofluid analyzer is configured to provide a cell parameter CP_k_i indicative of a cell type of the first cell CE_1 based on the classification of the first cell CE_1. The biofluid analyzer may be configured to determine, for each cell region CR_k_i of a cell region set SCR_i, a first cell parameter, also denoted as CP_k_i, where i is the number of selected images and k is the number of cell regions. Providing the cell parameter CP_k_i may include classifying the first cell CE_1_i associated with or represented by the first cell region CR_k_1 based on the first primary image data and / or the first secondary image data ID_1_2. The cell parameter CP_k_i may include, for example, cell type information. The cell parameter CP_k_i may include, for example, a cell type identifier indicating a cell type of the first cell. Providing the cell parameter CP_k_i may include determining the cell parameter CP_k_i based on the classification. Providing the cell parameter CP_k_i may include classifying the first cell CE_1_i associated with or represented by the first cell region CR_k_1 based on optical phenomena (optical phenomena of cells in neighboring image planes and / or image data acquired by different incident light settings, such as image data acquired by the first primary image data acquired by the first incident light setting ILS_1 and / or the first secondary image data acquired by the second incident light setting ILS_2). An advantage of acquiring first primary image data acquired by a first incident light setting ILS_1 and / or acquiring first secondary image data acquired by a second incident light setting ILS_2 is that additional information about the cells can be acquired.For example, different incident light settings can result in different optical phenomena. For example, cells can act as lenses, and depending on the incident light settings (e.g., angle) of the illumination light interrogating, e.g., a prepared biological fluid sample, a "lens effect" occurs. In other words, the combination of traditional cell features and "lens effect" can give more information about cells than cell features alone, e.g., when using reduced magnification and / or moderate quality optics, thus improving classification.

[0066] Providing the cell parameter CP_k_i may include classifying a first cell CE_1_i associated with or represented by the first cell region CR_k_1 based on information determined from the first primary image data and / or the first secondary image data ID_1_2.

[0067] In the one or more exemplary biofluid analyzers, classifying the cells CE includes applying a classification model to one or more of the first primary image data ID_1_1, the first secondary image data ID_1_2, and the first tertiary image data ID_1_3. In the one or more exemplary biofluid analyzers, classifying the cells CE includes applying a classification model to one or more of the first primary image data ID_1_1, the first secondary image data ID_1_2, the first tertiary image data ID_1_3, the second primary image data ID_2_1, the second secondary image data ID_2_2, and the second tertiary image data ID_2_3.

[0068] In one or more exemplary biofluid analyzers, the biofluid analyzer / processor comprises a classification circuit configured to operate according to one or more classification models. In one or more exemplary biofluid analyzers, providing the cellular parameters CP_k_i includes applying the classification model to one or more of the first primary image data ID_1_1, the first secondary image data ID_1_2, and the first tertiary image data ID_1_3. Classifying the cells in the prepared biofluid sample, such as in the cell region, can include determining whether the cell region and / or the cells satisfy one or more criteria, such as one or more features (e.g., cell features), of the classification model. Classifying the cells in the prepared biofluid sample, such as in the cell region, can include determining a cell type of the cells based on the classification model. The biofluid analyzer can be configured to extract one or more features, such as cell features, from the first primary image data, the first secondary image data, and / or the first tertiary image data, such as using the classification circuit. The cell features can represent one or more cells, a part of a cell, some parts of a cell, or an optical phenomenon associated with one or more cells. The biofluid analyzer can be configured to extract one or more features from the cell regions, e.g., using a classification circuit, e.g., to extract one or more features from a plurality of cell regions of a cell region set SCR_i. The extracted features can be provided by the biofluid analyzer as input to a classification model, e.g., to the classification circuit. The input to the classification model can include, e.g., the first primary image data ID_1_1, the first secondary image data ID_1_2, and / or the first tertiary image data ID_1_3, including a cell information vector, e.g., a cell feature vector. The classification circuit can include a neural network including one or more hidden layers. Each layer of the neural network can include one or more nodes.For example, classification of cells in a cell region may include comparing one or more features (e.g., extracted) present in the first primary image data ID_1_1, the first secondary image data ID_1_2, and / or the first tertiary image data ID_1_3 (e.g., extracted) with one or more model cells, e.g., cell regions, that contain cell features known to represent a particular type of cell and / or optical phenomenon. The classification model may be a neural network having an input layer, one or more hidden layers, e.g., multiple hidden layers, and an output layer. The input to the classification model may include a cell region (e.g., a cell region), multiple cell regions, and / or one or more stored features. For example, the input to the classification model may include a cell region having a cell region size of 20*20 μm, e.g., 41 when having a pixel resolution of 0.5 μm. 2 In one or more exemplary biofluid analyzers, the classification circuitry comprises a neural network for each cell type, e.g., cell class, and thus the output layer of the neural network can have one node. In one or more exemplary biofluid analyzers, the biofluid analyzer / processor comprises a neural network for all cell types of interest. Thereby, the output layer of the neural network can have one node per cell type or class. The output layer can be connected to a final hidden layer. The classification model can comprise an artificial neural network, ANN, e.g., a decision tree classifier. The classification model can be configured to detect and / or identify representative features, e.g., cellular features. For example, the classification model can perform interference detection of features across a cell population. The classification model can be configured to detect and / or identify representative features in a scatter flow cytometry scatter plot of the prepared biofluid sample.

[0069] In one or more exemplary biological fluid analyzers, the first image data ID_1 includes first composite image data based on the first primary image data ID_1_1 and / or the first secondary image data ID_1_2, e.g., based on the first tertiary image data ID_1_3, the second primary image data ID_2_1, the second secondary image data ID_2_2, and the second tertiary image data ID_2_3.

[0070] The biofluid analyzer may be configured to perform segmentation classification based on the first composite image, such as by using a classification model. For example, the biofluid analyzer may be configured to apply segmentation classification on one or more of the first primary image data ID_1_1, the first secondary image data ID_1_2, and the first tertiary image data ID_1_3. In other words, the biofluid analyzer may be configured to apply segmentation classification on one or more image segments of the first primary image data ID_1_1, the first secondary image data ID_1_2, and the first tertiary image data ID_1_3.

[0071] The classification model can be or include any suitable machine learning model suitable for classification. For example, the classification model can implement at least one machine learning model, such as at least one multi-layer perceptron (MLP), at least one convolutional neural network (CNN), at least one recurrent neural network (RNN), at least one autoencoder, at least one transformer, etc.

[0072] In one or more exemplary biofluid analyzers, the neural network may comprise a first hidden layer after the input layer. The input layer may be connected to the first hidden layer. The input layer may comprise a number of nodes equal to the length of the feature vector, e.g., the number of components. The first hidden layer may comprise at least three nodes, e.g., at least 20 nodes. In one or more exemplary neural networks, the first hidden layer may comprise in the range of 8-100 nodes, or in the range of 100-1,000 nodes, e.g., in the range of 200-500 nodes, e.g., about 300 nodes. In one or more exemplary neural networks, the neural network may comprise a second hidden layer after the first hidden layer. The second hidden layer may comprise at least five nodes, e.g., at least 20 nodes. The second hidden layer may optionally comprise in the range of 100-1,000 nodes, e.g., in the range of 8-100 nodes, or in the range of 200-500 nodes, e.g., about 300 nodes. In one or more exemplary neural networks, the neural network has fewer than 10 hidden layers, e.g., fewer than 5 hidden layers. The output / output layer of the neural network may include one or more output variables, e.g., at least 5 output variables. In one or more exemplary neural networks, the number of output variables is in the range of 6-15.

[0073] The biological fluid analyzer (e.g., acting as a server) can be configured to train a classification model based on one or more of the first primary image data ID_1_1, the first secondary image data ID_1_2, and the first tertiary image data ID_1_3 (e.g., based on one or more stored features).

[0074] In one or more exemplary biofluid analyzers, classifying cells in the prepared biofluid sample includes identifying a cell type of the cells. In one or more exemplary biofluid analyzers, classifying cells includes identifying a cell (such as one or more cells) in a first cell region. In other words, classifying cells includes identifying one or more groups of pixels representative of a cell in the cell region. In other words, classifying a first cell includes identifying one or more groups of pixels representative of a cell in one or more of the first primary image data ID_1_1, the first secondary image data ID_1_2, and the first tertiary image data ID_1_3.

[0075] The cell types disclosed herein can be considered, for example, as white blood cells (WBC), platelets (PLT), red blood cells (RBC), blood component clots, cellular debris, and / or external particles, such as dust, sediment, or residue from a container, etc. The cell types disclosed herein can be considered, for example, as mature cells, such as reticulocytes, lymphocytes, monocytes, segmented and band-shaped granulocytes, neutrophils, eosinophils, and / or basophils, and / or immature cells, such as normoblasts, erythroblasts, proerythroblasts, metamyelocytes, myelocytes, promyelocytes, myeloblasts, monoblasts, and / or lymphoblasts.

[0076] In one or more exemplary biofluid analyzers, classifying a cell includes determining a cell feature associated with the cell, e.g., a cell region including the cell. In one or more exemplary biofluid analyzers, classifying a cell includes determining a cell feature associated with the cell region based on the first primary image data ID_1_1 and / or the first secondary image data ID_1_2. In other words, classifying a cell includes determining a cell feature associated with the cell. Classifying a cell may include determining a plurality of cell features associated with the cell region, including a first cell feature, a second cell feature, etc. The cell feature may include one or more of a lobe feature, an area feature, a contrast feature, a roundness feature, a granularity feature, a cell nucleus feature, an optical feature, a cytoplasmic feature, a membrane feature, a morphology feature, and a function of one or more of the aforementioned features. In one or more exemplary biological fluid analyzers, the biological fluid analyzer is configured to classify cells based on a determination of a cellular feature that is one or more of a lobe feature, an area feature, a contrast feature, a roundness feature, a granularity feature, a nuclear feature, an optical feature, a cytoplasmic feature, a membrane feature, a morphology feature, and a function of one or more of the foregoing features. A cellular parameter, e.g., a first cellular parameter, can be determined based on the one or more cellular features.

[0077] In one or more exemplary biofluid analyzers, providing the cellular parameters of the first cell may include determining cellular parameters for a plurality of cellular regions of the cell region set based on the first primary image data ID_1_1 and / or the first secondary image data ID_1_2, and providing the plurality of cellular parameters. Providing the cellular parameters of the first cell may include determining cellular parameters for a plurality of cellular regions of the cell region set based on the first primary image data ID_1_1 and / or the first secondary image data ID_1_2, and providing the plurality of cellular parameters. In other words, the biofluid analyzer may be configured to determine cellular parameters for each of the cellular regions of the cell region set.

[0078] In one or more exemplary biofluid analyzers, the biofluid analyzer is configured to determine a cellular representation of the prepared biofluid sample based on a cellular parameter. In one or more exemplary biofluid analyzers, the biofluid analyzer is configured to determine a cellular representation of the prepared biofluid sample based on a plurality of cellular parameters. Determining the cellular representation of the prepared biofluid sample can include determining a cellular representation of an image plane, e.g., a first image plane, and / or a cellular representation of the image data. The cellular representation can be considered as a biofluid parameter of the prepared biofluid sample. Determining the cellular representation of the prepared biofluid sample can include determining a number of cellular regions that include the same or similar cellular parameters, cellular features, and / or cell types, e.g., determining one or more cell counts, e.g., white blood cell count and / or platelet count. Determining the cellular representation of the prepared biofluid sample can include determining a number of cellular regions that have the same or similar cellular parameters, e.g., one cellular parameter. The cellular representation can include one or more cell concentrations, e.g., white blood cell concentration and / or platelet concentration. The cell representation may include a white blood cell count or white blood cell concentration of different types of white blood cells, e.g., basophils, eosinophils, lymphocytes, monocytes, neutrophils, and / or a number or concentration of plastic beads. The first blood parameter may include a 3-differential WBC DIFF and / or a 5-differential WBC DIFF. Determining the cell representation may include counting the number of cell regions CR_k_i in one or more image planes IP_i having the same or similar cell parameters and / or cell features.

[0079] In one or more exemplary biofluid analyzers, the provision of cell parameters is based on cell features indicative of one or more of lobe features, area features, contrast features, roundness features, granularity features, cell nucleus features, optical features, and functions of one or more of the foregoing features. In one or more exemplary biofluid analyzers, the cell features include membrane features, geometric features, morphological features, and / or cell classification and / or type features. The membrane features can, for example, indicate information regarding the shape and / or texture of the cell membrane.

[0080] A lobe feature may be considered as a feature indicative of information about one or more lobes of a cell, such as the cell. For example, the lobe feature may include information about the lobe configuration of the cell. The lobe feature may indicate a segmentation of the cell, such as a blob and / or lobe segmentation of the cell. The lobe feature may indicate one or more of the number of nuclear lobes of the cell, the size of one or more lobes of the cell, the position of one or more lobes of the cell, and / or the size ratio between one or more lobes of the cell and the cell. A lobe feature included in a cell feature may be considered as a lobe cell feature. For example, the lobe feature may be considered as a feature indicative of information about one or more lobes of a plurality of cells of the prepared biological fluid sample. The lobe feature may indicate, for example, a cell that includes one, two, or three lobes.

[0081] The area feature may be considered as a feature indicative of information regarding the area of ​​a cell, such as the cell. For example, the area feature may include information regarding the area size of the cell. The area feature may indicate one or more of pixel size, pixel area, cell area, lobe area, and cell nucleus area. The area feature included in the cell feature may be considered as an area cell feature. For example, the area feature may be considered as a feature indicative of information regarding the area of ​​a plurality of cells of the prepared biological fluid sample. The area of ​​a cell may be 10 μm 2 ~350μm 2 The range may be:

[0082] The contrast feature may be considered as a feature indicative of information regarding the contrast of a cell, such as the cell. For example, the contrast feature may include information regarding the contrast value of the cell. The contrast feature may indicate one or more of the average pixel intensity of the cell nucleus and / or cytoplasm, and color parameters. The contrast feature included in the cell feature may be considered as a contrast cell feature. For example, the contrast feature may be considered as a feature indicative of information regarding the contrast of a plurality of cells of the prepared biological fluid sample. The contrast feature may be considered as the difference in light intensity between the image (e.g., in the cell region) and the adjacent background relative to the overall background intensity. The contrast of the cell may be in the range of 10% to 90%, for example 50%.

[0083] The roundness feature may be considered as a feature indicative of information regarding the roundness and / or circularity of a cell, such as the cell. For example, the roundness feature may include information regarding the roundness value and / or circularity value of the cell. The roundness feature included in the cell feature may be considered as a round cell feature. For example, the roundness feature may be considered as a feature indicative of information regarding the roundness of a plurality of cells of the prepared biological fluid sample. The roundness parameter may be calculated using the formula 4*Area(A)*Pi(π) / (÷)Perimeter^2(P 2 ) The roundness parameter can be in the range of 0 to 1, where 1 is the circularity of a perfect disk. For example, a cell region can be characterized as a platelet when it satisfies a roundness criterion, e.g., when the cell region is above a roundness threshold and / or when the cell region is within a roundness range. When detecting, identifying, classifying, and / or characterizing platelets, the roundness threshold can be, for example, 0.8.

[0084] The granularity feature may be considered as a feature that indicates information regarding the granularity and / or complexity of a cell, such as the cell. For example, the granularity feature may include information regarding the granularity value of the cell. The granularity feature may indicate one or more of side scatter measurement information, the internal complexity of the cell, and the cell type. The granularity feature included in the cell feature may be considered as a granular cell feature. For example, the granularity feature may be considered as a feature that indicates information regarding the granularity of a plurality of cells of the prepared biological fluid sample.

[0085] The cell nucleus feature may be considered as a feature indicative of information about a cell nucleus of a cell, such as the cell of interest. For example, the cell nucleus feature may include information about a cell nucleus size and / or type of the cell. The cell nucleus feature may be indicative of an area of ​​the cell nucleus. The cell nucleus feature included in the cell feature may be considered as a cell nucleus cell feature. For example, the cell nucleus feature may be considered as a feature indicative of information about the cell nuclei of a plurality of cells of the prepared biological fluid sample. The area of ​​the cell nucleus may be in the range of 0.1 μm to 5 μm, such as in the range of 0.5 μm to 2 μm.

[0086] The optical features can be considered as features that indicate information about optical phenomena of cells, e.g., optical phenomena in a cellular region that includes cells. For example, the optical features can include information about bright spots, e.g., bright spot detection, and / or dark spots, e.g., dark spot detection. The optical features included in the cell features can be considered as optical cell features. For example, the optical features can be considered as features that indicate information about optical phenomena of a plurality of cells of the prepared biological fluid sample.

[0087] The cell feature may be a function of different features derived, for example, from one or more of a lobe feature, an area feature, a contrast feature, a roundness feature, a granularity feature, an optical feature, and a cell nucleus feature.

[0088] In one or more exemplary biofluid analyzers, the biofluid analyzer is configured to classify cells based on one or more of a lobe feature, an area feature, a contrast feature, a roundness feature, a granularity feature, a nuclear feature, an optical feature, and a function of one or more of the aforementioned features. In other words, the biofluid analyzer can be configured to determine a cell type of a cell based on one or more of a lobe feature, an area feature, a contrast feature, a roundness feature, a granularity feature, a nuclear feature, and a function of one or more of the aforementioned features.

[0089] In one or more exemplary biofluid analyzers, classifying the first cell includes determining intermediate parameters, e.g., intermediate representations, using an autoencoder. In other words, the classification model can include one or more intermediate layers to provide one or more intermediate parameters of the cell, such as using an autoencoder.

[0090] In the one or more exemplary biological fluid analyzers, acquiring the image data includes acquiring second image data ID_2 associated with a second image plane IP_2. In the one or more exemplary biological fluid analyzers, acquiring the second image data ID_2 includes one or more of acquiring second primary image data ID_2_1 of a second image plane IP_2 within the prepared biological fluid sample, the second primary image data ID_2_1 being associated with a first incident light setting ILS_1, and acquiring second secondary image data ID_2_2 of the second image plane IP_2 of the prepared biological fluid sample, the second secondary image data ID_2_2 being associated with a second incident light setting ILS_2. In the one or more exemplary biological fluid analyzers, acquiring the second image data ID_2 includes acquiring second tertiary image data ID_2_3 of the second image plane IP_2 within the prepared biological fluid sample, the second tertiary image data ID_2_3 being associated with a third incident light setting ILS_3.

[0091] In one or more exemplary biological fluid analyzers, the second image plane is associated with a second height, also represented as H_2, within the prepared biological fluid sample. The second height to which the second image plane is associated may be, for example, a height on the z-axis relative to the bottom of the container / cuvette or the camera / microscope when the second primary image data, such as the second image, and / or the second secondary image data is acquired / captured. The second height H_2 may be different from the first height H_1.

[0092] In one or more exemplary biological fluid analyzers, the biological fluid analyzer is configured to classify cells in the prepared biological fluid sample based on one or more of the second primary image data ID_2_1 and the second secondary image data ID_2_2.

[0093] In one or more exemplary biological fluid analyzers, the biological fluid analyzer is configured to classify cells in a prepared biological fluid sample based on one or more of the first primary image data ID_1_1, the first secondary image data ID_1_2, the first tertiary image data ID_1_3, the second primary image data ID_2_1, the second secondary image data ID_2_2, and the second tertiary image data ID_2_3.

[0094] In one or more exemplary biological fluid analyzers, the biological fluid analyzer is configured to classify cells in the prepared biological fluid sample based on the second primary image data ID_2_1 and the first primary image data ID_1_2.

[0095] In one or more exemplary biological fluid analyzers, determining the cell region set SCR_i is based on second image data ID_2, for example one or more of the second primary image data ID_2_1 and the second secondary image data ID_2_2.

[0096] A computer program product is disclosed that includes a non-transitory computer readable medium having a computer program including program instructions that is loadable into a data processing unit and that, when executed by the data processing unit, is configured to perform operations according to any of the biological fluid analyzers disclosed herein.

[0097] Also disclosed is a method of classifying cells in a prepared biological fluid sample. The method can be performed by a biological fluid analyzer disclosed herein. The method includes acquiring image data of one or more image planes in an image stack in the prepared biological fluid sample, the image data including first image data. Acquiring the first primary image data includes acquiring first primary image data of the first image plane. The first primary image data is associated with a first incident light setting having a first angular light distribution. Acquiring the first primary image data includes acquiring first secondary image data of the first image plane. The first secondary image data is associated with a second incident light setting having a second angular light distribution. The method includes classifying cells in the prepared biological fluid sample based on the first primary image data and the first secondary image data. The method includes providing a cellular parameter associated with the cell based on the classification of the cell.

[0098] It should be understood that any discussion of features relating to the method is also applicable to corresponding features in the biofluid analyzer and / or system, and vice versa. 1 illustrates a schematic diagram of an exemplary biofluid analyzer BA, which comprises a memory, an interface, and one or more processors. The biofluid analyzer BA is configured to acquire, using an imaging module IM or the like, image data ID of one or more image planes of an image stack of a prepared biofluid sample, the image data ID including a first image data ID_1 associated with a first image plane IP_1. The biofluid analyzer BA is configured to acquire, using an imaging module IM or the like, a first primary image data ID_1_1 of the first image plane IP_1. The biofluid analyzer BA is configured to acquire, using an imaging module IM or the like, a first secondary image data ID_1_2 of the first image plane IP_1. The biofluid analyzer BA can be configured to determine a cell region set SCR_i based on the image data ID, using an imaging module IM or the like. Optionally, the biofluid analyzer may be configured to transmit image data, for example the first image data ID_1 (such as the first primary image data and / or the first secondary image data) and / or the cell region set SCR_i, included in the dataset from the image module IM to the feature extractor module FEM (302A). The feature extractor module FEM may be configured to extract one or more features from the image data ID, for example the first image data ID_1 and / or the cell region set SCR_i. For example, the feature extractor module FEM may be configured to extract one or more cellular features from the image data ID, for example the first image data ID_1 and / or the cell region set SCR_i. For example, the feature extractor module FEM may be configured to extract one or more cellular features from the first primary image data and / or the first secondary image data. Optionally, the biofluid analyzer may be configured to transmit the extracted one or more features from the feature extractor module FEM to the classification circuit module CCM (302B).

[0099] Optionally, the biofluid analyzer may be configured to transmit (302) the image data IDs contained in the dataset, for example the first image data ID_1 and / or the cell region set SCR_i, directly to the classification circuit module CCM.

[0100] The biofluid analyzer BA is configured to classify cells in the prepared biofluid sample based on the first primary image data ID_1 and the first secondary image data ID_1_2, using the classification circuit module CCM or the like, and provide cellular parameters associated with the cells (308). In other words, the biofluid analyzer BA can be configured to output the cellular parameters associated with the cells, using the classification circuit module CCM or the like, to, for example, a user, a database, and / or a server device (308).

[0101] In the one or more exemplary biological fluid analyzers, acquiring the first image data includes acquiring, such as with an imaging module IM, first tertiary image data ID_1_3 of a first image plane IP_1. In the one or more exemplary biological fluid analyzers, the first tertiary image data ID_1_3 is associated with a third incident light setting ILS_3 having a third angular light distribution ALD_3. In the one or more exemplary biological fluid analyzers, classifying cells in the prepared biological fluid sample is based on the first tertiary image data ID_1_3.

[0102] In one or more exemplary biological fluid analyzers, determining the cell region set SCR_i based on the image data ID includes determining a first cell region set SCR_1 belonging to a first image plane IP_1.

[0103] In one or more exemplary biological fluid analyzers, the biological fluid analyzer may be configured to classify a cell, such as a first cell CE_1, based on the first primary image data ID_1_1 and / or the first secondary image data ID_1_2, using a classification circuit module CCM or the like.

[0104] In the one or more exemplary biofluid analyzers, classifying the cells CE includes applying a classification model, such as with a classification circuit module CCM, to one or more of the first primary image data ID_1_1, the first secondary image data ID_1_2, and the first tertiary image data ID_1_3. In the one or more exemplary biofluid analyzers, classifying the cells CE includes applying a classification model, such as with a classification circuit module CCM, to one or more of the first primary image data ID_1_1, the first secondary image data ID_1_2, the first tertiary image data ID_1_3, the second primary image data ID_2_1, the second secondary image data ID_2_2, and the second tertiary image data ID_2_3.

[0105] In one or more exemplary biofluid analyzers, classifying cells in a prepared biofluid sample includes identifying a cell type of the cells, such as using a classification circuit module CCM.

[0106] In one or more exemplary biological fluid analyzers, acquiring the image data includes acquiring, such as using an imaging module IM, second image data ID_2 associated with a second image plane IP_2.

[0107] In the one or more exemplary biological fluid analyzers, acquiring the second image data ID_2 includes one or more of acquiring, using an imaging module IM or the like, second primary image data ID_2_1 of a second image plane IP_2 in the prepared biological fluid sample, the second primary image data ID_2_1 being associated with a first incident light setting ILS_1, and acquiring, using an imaging module IM or the like, second secondary image data ID_2_2 of the second image plane IP_2 of the prepared biological fluid sample, the second secondary image data ID_2_2 being associated with a second incident light setting ILS_2. In the one or more exemplary biological fluid analyzers, acquiring the second image data ID_2 includes acquiring, using an imaging module IM or the like, second tertiary image data ID_2_3 of the second image plane IP_2 in the prepared biological fluid sample, the second tertiary image data ID_2_3 being associated with a third incident light setting ILS_3.

[0108] In one or more exemplary biological fluid analyzers, the biological fluid analyzer is configured to classify cells in the prepared biological fluid sample based on one or more of the second primary image data ID_2_1 and the second secondary image data ID_2_2, such as using a classification circuit module CCM.

[0109] In one or more exemplary biological fluid analyzers, determining the cell region set SCR_i is based on second image data ID_2, for example one or more of the second primary image data ID_2_1 and the second secondary image data ID_2_2.

[0110] In one or more exemplary biological fluid analyzers, providing cellular parameters of the first cell includes determining cellular parameters for a plurality of cellular regions of the cellular region set based on the first primary image data ID_1_1 and / or the first secondary image data ID_1_2 using a classification circuit module CCM or the like, and providing a plurality of cellular parameters.

[0111] In one or more exemplary biofluid analyzers, the biofluid analyzer is configured to determine a cellular representation of the prepared biofluid sample based on the cellular parameters, such as using a classification circuit module CCM. In one or more exemplary biofluid analyzers, the biofluid analyzer BA can be configured to output the cellular representation, such as using a classification circuit module CCM, to a user, a database, and / or a server device (308).

[0112] In one or more exemplary biological fluid analyzers, the provision of cell parameters is based on cell features indicative of one or more of lobe features, area features, contrast features, roundness features, granularity features, cell nucleus features, optical features, and functions of one or more of the aforementioned features.

[0113] In one or more exemplary biological fluid analyzers, the first image data ID_1 includes first composite image data based on the first primary image data ID_1_1 and / or the first secondary image data ID_1_2, e.g., based on the first tertiary image data ID_1_3, the second primary image data ID_2_1, the second secondary image data ID_2_2, and the second tertiary image data ID_2_3.

[0114] 2 illustrates in schematic form an exemplary system 200, comprising a microscope 20, an image acquisition device (not shown, but e.g., implemented / integrated with the microscope), a prepared biological fluid sample in a container 22 (e.g., a cuvette, cavity), an exemplary imaging system 1 (such as the imaging system 1 of FIG. 3), and a biological fluid analyzer 10. Optionally, system 200 comprises an aperture device (e.g., comprised in imaging system 1) configured to apply an aperture and allow light from a first lens assembly to pass through the aperture to indirectly define a probe light.

[0115] The biofluid analyzer 10 is an exemplary biofluid analyzer according to the present disclosure. The biofluid analyzer 10 includes a memory 10A, an interface 10B, and one or more processors, such as a processor 10C. The biofluid analyzer 10 is configured to acquire image data ID of one or more image planes of an image stack in a prepared biofluid sample from an image acquisition device, such as via an interface 10B (14). The image data ID includes a first image data ID_1 associated with a first image plane IP_1. Optionally, the biofluid analyzer 10 can be configured to acquire image data from a network, such as a global network, for example the Internet or a telecommunications network. Optionally, the biofluid analyzer 10 can be configured to transmit information (such as cell parameters and / or cell representations) to the microscope 20 (14). For example, the biofluid analyzer 10 can be configured to acquire image data from a server device (not shown) via the network. The prepared biological fluid sample can be placed / positioned in a container 22, such as a cuvette, while image data ID, e.g., multiple images of the prepared biological fluid sample, e.g., a first image I_1, are acquired / acquired. The height to which the image plane is associated can be, e.g., a height on the z-axis relative to the bottom of the container 22 when the image is acquired / captured. The first image data can be associated with a first image plane IP_1, which can be associated with a first height H_1 within the prepared biological fluid sample. The image plane, across which the multiple images can extend, can be a two-dimensional plane, e.g., an xy plane relative to the z-axis. The image data ID can thus include multiple images associated with the image plane, each image plane separated from the next acquired / acquired image plane and / or the previously acquired / acquired image plane by a distance Δz. Δz can be a step increment for each acquired / acquired image. 2, 16 image planes are depicted, including a first image plane IP_1, a first distal image plane DIP_1, and a first proximal image plane PIP_1. The number of images and image planes can be increased to include, for example, at least 30, at least 40, or at least 100.The image data ID may include multiple images of the prepared biological fluid sample, each image may be associated with an image plane that is equidistant from the next acquired / acquired image plane and / or the previously acquired / acquired image plane. In other words, the image data ID may include a 3D image stack, such as a stack of images, each image of the image stack associated with an image plane that has a different associated height along the z-axis of the prepared biological fluid sample. The image data ID may include multiple images of a central portion 24 of the biological fluid sample. In other words, the image data ID may include multiple acquired / acquired images of the prepared biological fluid sample that represent an area or volume of the prepared biological fluid sample that is located away from an edge of the container 22 in which the prepared biological fluid sample is contained. Alternatively or additionally, the image data ID may include multiple acquired / acquired images of the prepared biological fluid sample that represent an area or volume of the entire container 22, e.g., the overall width and / or height of the container 22, e.g., including a window in the container 22. The biological fluid sample may include multiple cells, e.g., cells CE. For illustrative purposes, the container 22 and cells are enlarged and therefore not to scale. In the example shown in FIG. 2, the cells CE represent white blood cells WBC. Smaller cells, such as cell CE_10, can be, for example, platelets.

[0116] Each image of the image data ID may include multiple representations. The multiple representations may include multiple particles, such as cells, such as white blood cells WBC, platelets, red blood cells RBC, and / or foreign particles, such as dust or residue from the container. Acquiring (14) the first image data ID_1 includes acquiring (14) first primary image data ID_1_1 of a first image plane IP_1. The first primary image data ID_1_1 is associated with a first incident light setting ILS_1. The first incident light setting ILS_1 may optionally have a first angular light distribution. Acquiring (14) the first image data ID_1 includes acquiring (14) first secondary image data ID_1_2 of the first image plane IP_1. The first secondary image data ID_1_2 is associated with a second incident light setting ILS_2. The second incident light setting ILS_2 may optionally have a second angular light distribution.

[0117] The biofluid analyzer 10 is configured to classify cells in the prepared biofluid sample based on the first primary image data ID_1_1 and / or the first secondary image data ID_1_2, such as with a processor 10C, and provide cellular parameters CP associated with the cells. In one or more exemplary systems and / or biofluid analyzers, the biofluid analyzer 10 is configured to output the cellular parameters CP and / or cellular representations, such as with a processor 10C and / or via an interface 10B, to, for example, a user U, a database, and / or a server device (12).

[0118] In the one or more exemplary biological fluid analyzers, acquiring the image data includes acquiring second image data ID_2 associated with a second image plane IP_2. In the one or more exemplary biological fluid analyzers, acquiring the second image data ID_2 includes one or more of acquiring second primary image data ID_2_1 of a second image plane IP_2 within the prepared biological fluid sample, the second primary image data ID_2_1 being associated with a first incident light setting ILS_1, and acquiring second secondary image data ID_2_2 of the second image plane IP_2 of the prepared biological fluid sample, the second secondary image data ID_2_2 being associated with a second incident light setting ILS_2. In the one or more exemplary biological fluid analyzers, acquiring the second image data ID_2 includes acquiring second tertiary image data ID_2_3 of the second image plane IP_2 within the prepared biological fluid sample, the second tertiary image data ID_2_3 being associated with a third incident light setting ILS_3. The second image plane IP_2 can be a proximal image plane PIP, e.g., a first proximal image plane, and / or a distal image plane DIP, e.g., a first distal image plane DIP_1. In one or more exemplary biological fluid analyzers, the second image plane IP_2 is associated with a second height, also represented as H_2, within the prepared biological fluid sample. The second height H_2 to which the second image plane IP_2 is associated can be a height, e.g., on the z-axis, relative to the bottom of the container / cuvette or the camera / microscope when the second primary image data, e.g., the second image, and / or the second secondary image data, e.g., the second image, is acquired / captured. The second height H_2 can be different from the first height H_1.

[0119] In one or more exemplary biological fluid analyzers, the biological fluid analyzer is configured to classify cells in a prepared biological fluid sample based on one or more of the second primary image data ID_2_1 and the second secondary image data ID_2_2, using a processor 10C or the like.

[0120] In one or more exemplary systems and / or biological fluid analyzers, the first proximal image plane PIP_1 is associated with a first proximal height PH_1 within the prepared biological fluid sample, the first proximal height PH_1 being different from the first height H_1.

[0121] A first distal distance, also represented as DD_1, between the first image plane IP_1 and the first distal image plane DIP_1, and a first proximal distance, also represented as PD_1, between the first image plane IP_1 and the first proximal image plane PIP_1, are in the range of 1 μm to 75 μm. In FIG. 2, the first distal distance DD_1 is greater than the first proximal distance PD_1.

[0122] The biological fluid analyzer 10 can be configured to perform any of the methods disclosed in FIG. The biological fluid analyzer 10, e.g., processor 10C, is optionally configured to perform any of the operations disclosed in Figure 4 (e.g., any one or more of S102C, S102D, S102D1, S102D2, S102D3, S104A, S108A, S108B, S108C, S108D, S110A, S112). The operations of the biological fluid analyzer may be embodied in the form of executable logic routines (e.g., lines of instructions, software programs, etc.) stored in a non-transitory computer-readable medium (e.g., memory 10A) and executed by processor 10C.

[0123] Moreover, the operations of the biological fluid analyzer 10 may be viewed as methods that the biological fluid analyzer 10 is configured to perform. And, although the functions and operations described may be implemented in software, such functions may equally be implemented via dedicated hardware or firmware, or some combination of hardware, firmware, and / or software.

[0124] FIG. 3 illustrates an exemplary imaging system 1. The imaging system 1 can be an example of an imaging system used to acquire image data, such as the first image data ID_1, such as the first primary image data ID_1_1, the first secondary image data ID_1_2, the first tertiary image data ID_1_3, the second primary image data ID_2_1, the second secondary image data ID_2_2, and / or the second tertiary image data ID_2_3 disclosed herein. The imaging system 1 comprises a light source assembly 3 and a first lens assembly 4. An optical axis 2 of the imaging system 1 extends through the light source assembly 3 and the first lens assembly 4. In the embodiment of FIG. 3, the first lens assembly 4 comprises a first lens 41, a second lens 42, and an optional third lens 43. The lenses 41, 42, 43 are configured to direct light emitted by the light source assembly 3 toward a convergence point located downstream of the first lens assembly 4.

[0125] The system 1 of Figure 3 can include an aperture device 5 configured to apply an aperture to allow light from the first lens assembly 4 to pass through the aperture. In the exemplary biological fluid analyzer of Figure 3, the aperture device 5 extends along a back focal plane of the first lens assembly 4. A convergence point of light directed by the first lens assembly 4 is located at the back focal plane of the first lens assembly 4. Thus, the applied aperture can be selectively applied to include one or more of the convergence points.

[0126] In the embodiment of Fig. 3, the light source assembly comprises one or more of a first light source 31, a second light source 32, and a third light source 33. The first light source 31 is disposed on the optical axis 2 of the imaging system 1. The beams of light emitted by each of the light sources 31, 32, 33 converge to a convergence point on the back focal plane of the first lens assembly 4. In the configuration of Fig. 3, an applied aperture in the aperture device allows the light emitted by the three light sources 31, 32, 33 to pass through the applied aperture.

[0127] Downstream of the aperture device 5, the imaging device 1 of the system 1 of FIG. 3 comprises a second lens assembly 6. In the system 1, the second lens assembly 6 comprises a first lens 62 and / or a second lens 62. The second lens assembly 6 is configured to direct the light passing through the applied aperture in the aperture device 5 and focus the light on the back focal plane of the second lens assembly 6. FIG. 3 shows that each convergence point on the back focal plane of the first lens assembly 4 produces a different angle of incidence on the back focal plane of the second lens assembly 6. Thus, the angle of incidence of the light directed by the second lens assembly 6 onto the back focal plane of the second lens assembly 6 can be customized by applying the aperture of the aperture device 5.

[0128] In the embodiment of FIG. 3, a probe volume 7 is defined along the back focal plane of the second lens assembly 6. The probe volume 7 of FIG. 3 receives light with three different angles of incidence. The light received by the probe volume 7 is referred to as the probe light. The light passing through the applied aperture indirectly defines the probe light, since the light passing through the applied aperture is directed by the second lens assembly 6 to define the probe light. In the embodiment of FIG. 3, the probe volume 7 is optionally received by a multi-purpose cuvette (not shown). In such an example, the biofluid analyzer 1 can be configured as a multi-purpose device with the necessary fluidic systems and mechanisms (not shown) for performing multiple measurements. Such an exemplary biofluid analyzer 1 can further comprise a solution pack containing one or more solutions (not shown), and the exemplary biofluid analyzer 1 is configured to manage and release the solution pack to automatically at least partially prepare a prepared body fluid sample after aspiration by using its fluidic systems and mechanisms, and to control the fluidic system to execute a cleaning program of at least the multi-purpose cuvette before and / or after the biofluid analysis.

[0129] The incidence angle or angles of the probe light can be customized by the aperture device 5. To achieve such customization, the system 1 comprises a controller 8 configured to control the aperture device 5.

[0130] The system 1 comprises a controller 8 configured to control parts of the imaging system 1. For example, the controller 8 may be configured to control the aperture device via one or more aperture control signals to the aperture device 5.

[0131] 3, the aperture device 5 is controlled by the controller 8 according to an aperture configuration in which the applied aperture has a second aperture configuration. In other words, the controller sends a second aperture control signal to the aperture device 5. As explained above, the second aperture configuration allows light converging at three convergence points on the back focal plane of the first lens assembly 4 to pass through the applied aperture.

[0132] Because the aperture device 5 in this embodiment extends along the rear focal plane of the first lens assembly 4, the aperture device 5 can perform precise filtering of the light directed by the first lens assembly 4 by selectively applying an aperture to include one or more of the convergence points located on the rear focal plane of the first lens assembly 4. However, the aperture device 5 can also extend along a plane different from the rear focal plane of the first lens assembly 4 and still perform suitable filtering of the light directed by the first lens assembly 4.

[0133] 4 shows a flow diagram of an exemplary method. A method 100 for classifying cells in a prepared biological fluid sample is shown. The method 100 can be performed by a biological fluid analyzer disclosed herein, such as the biological fluid analyzer BA of FIG. 1 and / or the biological fluid analyzer 10 of FIG. 2. The method 100 includes a step S102 of acquiring image data, also represented by IDs of one or more image planes of an image stack in the prepared biological fluid sample. The image data ID includes first image data ID_1 associated with a first image plane IP_1.

[0134] The step S102 of acquiring the first image data ID_1 comprises a step S102A of acquiring first primary image data ID_1_1 of a first image plane IP_1. The first primary image data ID_1_1 is associated with a first incident light setting having a first angular light distribution. The step S102 of acquiring the first image data ID_1 comprises a step S102B of acquiring first secondary image data ID_1_2 of the first image plane IP_1. The first secondary image data ID_1_2 is associated with a second incident light setting having a second angular light distribution.

[0135] The method 100 includes a step S108 of classifying cells CE in the prepared biological fluid sample based on the first primary image data ID_1_1 and / or the first secondary image data ID_1_2.

[0136] The method 100 includes a step S110 of providing a cell parameter CP associated with the cell CE based on the classification S108 of the cell CE. In one or more exemplary methods, the step S102 of acquiring first image data ID_1 includes a step S102C of acquiring first tertiary image data ID_1_3 of a first image plane IP_1, the first tertiary image data ID_1_2 being associated with a third incident light setting having a third angular light distribution.

[0137] In one or more exemplary methods, the step S108 of classifying the cell CE is based on the first tertiary image data ID_1_3. In one or more exemplary methods, the method 100 includes a step S104 of determining a cell region set SCR_i based on the image data ID.

[0138] In one or more exemplary methods, step S104 of determining the cell region set SCR_i includes step S104A of determining the cell region set SCR_i, such as a first cell region set SCR_1 belonging to a first image plane IP_1, based on first image data ID_1.

[0139] In one or more exemplary methods, the step S108 of classifying the cell CE includes a step S110A of classifying a cell in a first cell region of the cell region set (S108A) and providing a first cell parameter CP_1 indicative of a cell type of the cell.

[0140] In one or more exemplary methods, the step S108 of classifying the cell CE includes a step S108B of applying a classification model to one or more of the first primary image data ID_1_1, the first secondary image data ID_1_2, and the first tertiary image data ID_1_3.

[0141] In one or more exemplary methods, classifying the cell CE, S108, includes identifying a cell type of the cell CE, S108C. In one or more exemplary methods, acquiring image data ID includes acquiring second image data ID_2 associated with a second image plane IP_2. In one or more exemplary methods, acquiring second image data ID_2 includes acquiring second primary image data ID_2_1 of a second image plane IP_2 in the prepared biological fluid sample, the second primary image data ID_2_1 being associated with a first incident light setting. In one or more exemplary methods, acquiring second image data ID_2 includes acquiring second secondary image data ID_2_2 of a second image plane IP_2 in the prepared biological fluid sample, the second secondary image data ID_2_2 being associated with a second incident light setting. In one or more exemplary methods, the step S102D of acquiring the second image data ID_2 includes a step S102D3 of acquiring second tertiary image data ID_2_3 of a second image plane IP_2 within the prepared biological fluid sample, the second tertiary image data ID_2_3 being associated with a third-order incident light setting.

[0142] In one or more exemplary methods, the step S108 of classifying the cells CE includes a step S108D of classifying the cells in the prepared biological fluid sample based on one or more of the second primary image data ID_2_1 and the second secondary image data ID_2_2.

[0143] In one or more exemplary methods, the step S104 of determining the cell region set SCR_i is based on the second image data ID_2. In one or more exemplary methods, the step S110 of providing a cellular parameter CP includes determining cellular parameters CP for a plurality of cellular regions of the cellular region set SCR_i based on the first primary image data ID_1_1 and the first secondary image data ID_1_2 (S110A) and providing a plurality of cellular parameters CP_k_1.

[0144] In one or more exemplary methods, the method 100 includes a step S112 of determining a cellular representation CP of the prepared biological fluid sample based on the cellular parameters, for example based on a plurality of first cellular parameters CP_k_1.

[0145] In one or more exemplary methods, step S110 of providing the cell parameter CP is based on cell features indicative of one or more of a lobe feature, an area feature, a contrast feature, a roundness feature, a granularity feature, a cell nucleus feature, an optical feature, and functions of one or more of the aforementioned features.

[0146] 5-9 show exemplary images of cells (e.g., cell regions) acquired at different image planes (along the X-axis) and different incident light settings (along the Y-axis) in which, for example, exemplary methods and / or biofluid analyzers according to the present disclosure in which the techniques disclosed herein are implemented and / or used. Each of FIGS. 5-9 shows a series of seven images, e.g., image tiles, for each of 15 image planes (IP_1-IP_15), which include cell regions acquired at 15 different image planes and seven different incident light settings. The fourth tile T_4 in FIGS. 5-9 can be considered as the first primary image data ID_1_1 of the first image plane IP_1 acquired by a first incident light setting ILS_1 having a first angular light distribution ALD_1. The first tile T_1 in Figures 5-9 can be considered as the first secondary image data ID_1_2 of the first image plane IP_1 acquired by the second incident light setting ILS_2 with the second angular light distribution ALD_2. The seventh tile T_7 in Figures 5-9 can be considered as the first tertiary image data ID_1_3 of the first image plane IP_1 acquired by the third incident light setting ILS_3 with the third angular light distribution ALD_3.

[0147] The eleventh tile T_11 in Figs. 5-9 can be considered as the second primary image data ID_2_1 of the second image plane IP_2 acquired by the first incident light setting ILS_1 with the first angular light distribution ALD_1. The eighth tile T_8 in Figs. 5-9 can be considered as the second secondary image data ID_2_2 of the second image plane IP_2 acquired by the second incident light setting ILS_2 with the second angular light distribution ALD_2. The fourteenth tile T_14 in Figs. 5-9 can be considered as the second tertiary image data ID_2_3 of the second image plane IP_1 acquired by the third incident light setting ILS_3 with the third angular light distribution ALD_3.

[0148] Figure 5 shows a series of seven images, e.g., image tiles, for each image plane containing cellular regions in 15 different image planes with an inter-plane distance of about 5 μm. The cells observed in Figure 5 are classified as eosinophils.

[0149] Figure 6 shows a series of seven images, e.g., image tiles, for each image plane of an image stack, the seven images including cellular regions in 15 different image planes with an inter-plane distance of about 5 μm. The cells observed in Figure 6 are classified as lymphocytes.

[0150] Figure 7 shows a series of seven images, e.g., image tiles, for each image plane of an image stack, where the seven images include cellular regions in 15 different image planes with an inter-plane distance of about 5 μm. The cells observed in Figure 7 have been classified as monocytes.

[0151] Figure 8 shows a series of seven images, e.g., image tiles, for each image plane of an image stack, the seven images containing cellular regions in 15 different image planes with an inter-plane distance of about 5 μm. The cells observed in Figure 8 are classified as neutrophils.

[0152] 9 shows a series of seven images, e.g., image tiles, for each image plane of an image stack, the seven images including cellular regions in 15 different image planes with an inter-plane distance of about 5 μm. The cells observed in FIG. 9 are classified as platelets.

[0153] The use of terms such as "first", "second", "third", and "fourth", "primary", "secondary", "tertiary", etc., does not imply any particular order, but is included to identify individual elements. Additionally, the use of terms such as "first", "second", "third", and "fourth", "primary", "secondary", "tertiary", etc., does not imply any order or importance, but rather terms such as "first", "second", "third", and "fourth", "primary", "secondary", "tertiary", etc. are used to distinguish one element from another. It should be noted that words such as "first", "second", "third", and "fourth", "primary", "secondary", "tertiary", etc., are used throughout this specification for labeling purposes only, and are not intended to indicate any particular spatial or temporal order.

[0154] The memory may be one or more of a buffer, a flash memory, a hard drive, a removable media, a volatile memory, a non-volatile memory, a random access memory (RAM), or other suitable devices. In a typical configuration, the memory may include a non-volatile memory for long-term data storage and a volatile memory that serves as a system memory for the processor. The memory may exchange data with the processor via a data bus. The memory may be considered a non-transitory computer-readable medium.

[0155] The memory can be configured to store information (such as information indicative of cellular parameters, image data, cellular representations, and / or cellular characteristics) in a portion of the memory. Additionally, the labeling of a first element does not imply the presence of a second element, and vice versa.

[0156] It will be appreciated that Figures 1-9 include some modules or operations shown with solid lines and some modules or operations shown with dashed lines. The modules or operations included in the solid lines are modules or operations included in the broadest exemplary embodiment. The modules or operations included in the dashed lines are exemplary embodiments that may be included in the solid line exemplary embodiment, or may be part of the solid line exemplary embodiment, or may be additional modules or operations that may be incorporated in addition to the modules or operations of the solid line exemplary embodiment. It will be appreciated that these operations do not have to be performed in the order presented. Furthermore, it will be appreciated that not all of these operations need to be performed. The exemplary operations may be performed in any order and in any combination.

[0157] It should be noticed that the word "comprising" does not necessarily exclude the presence of other elements or steps than those listed. It should be noted that the word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements.

[0158] It should further be noted that any reference signs are not intended to limit the scope of the claims, and that the exemplary embodiments can be implemented at least partly by means of both hardware and software, and that several "means", "units" or "devices" can be represented by the same item of hardware.

[0159] As used herein, expressions of degree, such as the terms "approximately", "about", "generally", and "substantially", refer to values, amounts, or characteristics close to the stated value, amount, or characteristic that still performs a desired function or achieves a desired result. For example, the terms "approximately", "about", "generally", and "substantially" can refer to amounts within 10% or less, within 5% or less, within 1% or less, within 0.1% or less, and within 0.01% or less of the stated amount. When the stated amount is 0 (e.g., none, not having), the above ranges may be specific ranges rather than within a specific % of the value. For example, within 10% or less by weight / volume, within 5% or less by weight / volume, within 1% or less by weight / volume, within 0.1% or less by weight / volume, and within 0.01% or less by weight / volume of the stated amount.

[0160] Various exemplary methods, devices, and systems described herein are described in the general context of method step processes, which may be implemented in one aspect by a computer program product, which may be embodied in a computer-readable medium including computer-executable instructions such as program code, and which may be executed by a computer in a network environment. Computer-readable media may include removable and non-removable storage devices, including, but not limited to, read-only memory (ROM), random access memory (RAM), compact discs (CDs), digital versatile discs (DVDs), and the like. Generally, program modules may include routines, programs, objects, components, data structures, and the like that perform specified tasks or implement particular abstract data types. Computer-executable instructions, associated data structures, and program modules represent examples of program code for executing steps of the methods disclosed herein. The particular sequence of such executable instructions or associated data structures represents examples of corresponding acts for implementing the functions described in such steps or processes.

[0161] While features have been illustrated and described, they are not intended to be limiting of the claimed invention, and it will be understood that it will be apparent to those skilled in the art that various changes and modifications can be made without departing from the spirit and scope of the claimed invention. Accordingly, the specification and drawings are to be interpreted in an illustrative rather than restrictive sense. The claimed invention is intended to cover all alternatives, modifications, and equivalents.

[0162] Examples of methods and products (biofluid analyzers and systems) according to the present disclosure are described in the following sections. Item 1. A biological fluid analyzer comprising a memory, an interface, and one or more processors, acquiring image data for one or more image planes of an image stack within the prepared biological fluid sample, the image data including first image data associated with the first image plane, and acquiring the first image data; acquiring first primary image data of a first image plane, the first primary image data being associated with a first incident light setting having a first angular light distribution; and acquiring first secondary image data of a first image plane, the first secondary image data being associated with a second incident light setting having a second angular light distribution; A biofluid analyzer configured to classify cells in the prepared biofluid sample based on the first primary image data and the first secondary image data, and provide cellular parameters associated with the cells.

[0163] Item 2. The biological fluid analyzer of item 1, wherein acquiring first image data includes acquiring first tertiary image data of a first image plane, the first tertiary image data being associated with a third incident light setting having a third angular light distribution, and classifying cells in the prepared biological fluid sample is based on the first tertiary image data.

[0164] Item 3. The biofluid analyzer of any of the preceding items, wherein the biofluid analyzer is configured to determine a cell region set based on the image data. Item 4. The biofluid analyzer of item 3, wherein determining the cell region set is based on the first image data.

[0165] Item 5. A biofluid analyzer as described in Item 3 or 4, wherein determining a cell region set includes determining a first cell region set belonging to a first image plane based on the first image data.

[0166] Item 6. The biofluid analyzer of any of items 3 to 5, wherein classifying cells in the prepared biofluid sample includes classifying cells in a first cell region of the cell region set and providing a first cell parameter indicative of a cell type of the cells.

[0167] Item 7. The biofluid analyzer of any of the preceding items, wherein classifying the cells in the prepared biofluid sample includes applying a classification model to one or more of the first primary image data, the first secondary image data, and the first tertiary image data.

[0168] Item 8. The biofluid analyzer of any of the preceding items, wherein classifying cells in the prepared biofluid sample includes identifying a cell type of the cells. Item 9. The biological fluid analyzer of any of the preceding items, wherein the first incident light setting includes one or more of a lens assembly setting, an aperture setting, an angle setting, and a light source setting, and the first incident light setting is configured to provide incident light including a first light component, the first light angle being greater than the first angle.

[0169] Item 10. The biological fluid analyzer of any of the preceding items, wherein the first incident light setting includes an aperture setting indicating a first aperture size used to acquire the first primary image data. Item 11. The biological fluid analyzer of any of the preceding items, wherein the second incident light setting includes one or more of a lens assembly setting, an aperture setting, an angle setting, and a light source setting, and the second incident light setting is configured to provide incident light including a second light component where the second light angle is greater than the second angle.

[0170] Item 12. The biological fluid analyzer of any of the preceding items, wherein the second incident light setting is configured to provide an incident light angle that does not overlap with the incident light angle provided by the first incident light setting. Item 13. The biological fluid analyzer of any of the preceding items, wherein the second incident light setting includes an aperture setting indicating a second aperture size used to acquire the first secondary image data.

[0171] Item 14. The biofluid analyzer of any of items 10 to 13, wherein the first aperture size is different from the second aperture size and / or the shape of the first aperture is different from the shape of the second aperture.

[0172] Item 15. The biological fluid analyzer of any of the preceding items, wherein the first incident light setting and / or the second incident light setting includes an aperture setting exhibiting a numerical aperture in the range of 0 to 0.7. Item 16. The method of acquiring image data, comprising: acquiring second image data associated with a second image plane; and acquiring the second image data, - acquiring second primary image data of a second image plane within the prepared biological fluid sample, the second primary image data being associated with the first incident light setting; and - acquiring second secondary image data of a second image plane of the prepared biological fluid sample, the second secondary image data being acquired with a second incident light setting, wherein the biological fluid analyzer comprises one or more of: - The biofluid analyzer of any of the preceding items, configured to classify cells in the prepared biofluid sample based on one or more of the second primary image data and the second secondary image data.

[0173] Item 17. The biological fluid analyzer according to item 16, which is dependent on item 3, wherein determining the cell region set is based on the second image data. Item 18. A biofluid analyzer according to any of the preceding items, wherein providing cellular parameters includes determining cellular parameters for a plurality of cellular regions of the cellular region set based on the first primary image data and the first secondary image data, and providing a plurality of cellular parameters.

[0174] Item 19. The biofluid analyzer of any of the preceding items, wherein the biofluid analyzer is configured to determine the cellular representation of the prepared biofluid sample based on the cellular parameters. Item 20. The biofluid analyzer of any of the preceding items, wherein the provision of the cell parameters is based on cell features indicative of one or more of a lobe feature, an area feature, a contrast feature, a roundness feature, a granularity feature, a nuclear feature, an optical feature, and a function of one or more of the aforementioned features.

[0175] Item 21. The biological fluid analyzer of any of the preceding items, wherein the first image data includes first composite image data based on the first primary image data and / or the first secondary image data.

[0176] Item 22. A computer program product comprising a non-transitory computer-readable medium having a computer program including program instructions, the computer program being loadable into a data processing unit and configured to perform the operations of any of items 1 to 21 when the computer program is executed by the data processing unit.

[0177] Item 23. A method for classifying cells in a prepared biological fluid sample, comprising: - acquiring image data for one or more image planes of an image stack within the prepared biological fluid sample, the image data including first image data associated with a first image plane, the acquiring of the first image data including: - acquiring first primary image data of a first image plane, the first primary image data being associated with a first incident light setting having a first angular light distribution; and - acquiring first secondary image data of a first image plane, the first secondary image data being acquired with a second incident light setting having a second angular light distribution; - classifying cells in the prepared biological fluid sample based on the first primary image data and the first secondary image data; - providing a cellular parameter associated with the cell based on the classification of the cell. [Explanation of symbols]

[0178] U user 200 Systems 1. Imaging system 12 Output 14 Transmission / Acquisition 10. Biofluid Analyzer 10A Memory 10B Interface 10C processor 20. Microscope 22 Containers / cuvettes 24 Center part 302, 302A, 302B Transmission 308 Output BA Biofluid Analyzer PIP_i Proximal Image Plane PIP_1 First proximal image plane IP_i image plane IP_1 First image plane IP_2 Second image plane DIP_i Distal Image Plane DIP_1 First distal image plane PH_i Proximal height PH_1 First proximal height H_i Height H_1 First height H_2 Second height DH_i Distal height DH_1 First Distal Height PD_i Proximal Distance DD_i Distal distance PD_1 1st Proximal Distance DD_1 First Distal Distance CE cells Δz step increment zz axis ID Image data ID_1 1st primary image data I_i Image IM Image Module FEM Feature Extraction Module CCM Classification Circuit Module PI_i proximal image DI_i Distal Image SCR_i cell region set CR_k_i cell area CP_k_i Cell parameters C_i Number of cell regions ID_1_1 1st primary image data ID_1_2 1st secondary image data ID_1_2 First 3D image data ID_2_1 Second primary image data ID_2_2 Second secondary image data ID_2_3 Second 3D image data ILS incident light settings ILS_1 First incident light setting ILS_2 Second incident light setting ILS_3 Third incident light setting ALD angular light distribution ALD_1 First Angular Light Distribution ALD_2 Second Angular Light Distribution ALD_3 Third angle light distribution A Angle A_1 First angle A_2 Second angle AS aperture size AP_1 First aperture AP_2 Second aperture AP_3 3rd aperture AS_1 First aperture size AS_2 Second aperture size LC light component LC_1 First light component LC_2 Second light component LC_3 Third light component

Claims

1. 1. A biological fluid analyzer comprising a memory, an interface, and one or more processors, acquiring image data for one or more image planes of an image stack within the prepared biological fluid sample, the image data including first image data associated with a first image plane, and acquiring the first image data includes: acquiring first primary image data of the first image plane, the first primary image data being associated with a first incident light setting having a first angular light distribution; and acquiring first secondary image data of the first image plane, the first secondary image data being associated with a second incident light setting having a second angular light distribution; and classifying cells in the prepared biological fluid sample based on the first primary image data and the first secondary image data, and providing cellular parameters associated with the cells.

2. 2. The biological fluid analyzer of claim 1, wherein acquiring first image data includes acquiring first tertiary image data of the first image plane, the first tertiary image data being associated with a third incident light setting having a third angular light distribution, and wherein classifying cells in the prepared biological fluid sample is based on the first tertiary image data.

3. The biofluid analyzer of claim 1 or 2, wherein the biofluid analyzer is configured to determine a set of cell regions based on the image data.

4. The biofluid analyzer of claim 3 , wherein determining the set of cell regions is based on the first image data.

5. The biofluid analyzer of claim 3 or 4, wherein determining the set of cell regions comprises determining a first set of cell regions belonging to the first image plane based on the first image data.

6. 6. The biofluid analyzer of claim 3, wherein classifying cells in the prepared biofluid sample comprises classifying cells in a first cell region of the set of cell regions and providing a first cell parameter indicative of a cell type of the cells.

7. 7. The biological fluid analyzer of claim 1, wherein classifying cells in the prepared biological fluid sample comprises applying a classification model to one or more of the first primary image data, the first secondary image data, and the first tertiary image data.

8. The biofluid analyzer of claim 1 , wherein classifying cells in the prepared biofluid sample comprises identifying a cell type of the cells.

9. Acquiring the image data includes acquiring second image data associated with a second image plane, and acquiring the second image data includes: acquiring second primary image data of a second image plane within the prepared biological fluid sample, the second primary image data being associated with the first incident light setting; and acquiring second secondary image data of the second image plane of the prepared biological fluid sample, the second secondary image data being acquired with the second incident light setting, wherein the biological fluid analyzer comprises one or more of: A biological fluid analyzer according to any one of claims 1 to 8, configured to classify cells in the prepared biological fluid sample based on one or more of the second primary image data and the second secondary image data.

10. 10. The biofluid analyzer of claim 9 when dependent on claim 3, wherein determining the set of cell regions is based on the second image data.

11. 11. The biofluid analyzer of claim 1, wherein providing cellular parameters includes determining cellular parameters for a plurality of cellular regions of the set of cellular regions based on the first primary image data and the first secondary image data, and providing a plurality of cellular parameters.

12. The biofluid analyzer of claim 1 , wherein the biofluid analyzer is configured to determine a cellular representation of the prepared biofluid sample based on the cellular parameters.

13. 13. The biological fluid analyzer of claim 1, wherein the provision of the cell parameters is based on cell features indicative of one or more of a lobe feature, an area feature, a contrast feature, a roundness feature, a granularity feature, a nuclear feature, an optical feature, and a function of one or more of the aforementioned features.

14. 14. The biological fluid analyzer of claim 1, wherein the first image data comprises first composite image data based on the first primary image data and / or the first secondary image data.

15. 15. A computer program product comprising a non-transitory computer readable medium having a computer program including program instructions, the computer program being loadable into a data processing unit and configured to perform the operations of any of claims 1 to 14 when the computer program is executed by the data processing unit.

16. 1. A method for classifying cells in a prepared biological fluid sample, comprising: acquiring image data for one or more image planes of an image stack within the prepared biological fluid sample, the image data comprising first image data associated with a first image plane, the acquiring of the first image data comprising: acquiring first primary image data of the first image plane, the first primary image data being associated with a first incident light setting having a first angular light distribution; and - acquiring first secondary image data of said first image plane, said first secondary image data being acquired with a second incident light setting having a second angular light distribution; - classifying cells in the prepared biological fluid sample based on the first primary image data and the first secondary image data; - providing a cellular parameter associated with said cell based on said classification of said cell.

Images