Cell selection methodology

Abstract

A computer implemented method may be used for populating a user interface with images from analysis of a biological sample. Such a method may include receiving images of cells from the biological sample. In these images, there may be both first cell-type images and second cell-type images, where the second cell-type is a subset of the first cell-type. The method may also include establishing first and second cell type counts, and assigning a number of display images to the second cell-type. This number of display images may be based on a relationship of the second cell-type count to the first cell-type count.

Description

CELL SELECTION METHODOLOGYBACKGROUND

[0001] Blood cell analysis is one of the most commonly performed medical tests for providing an overview of a patient's health status. A blood sample can be drawn from a patient's body and stored in a test tube containing an anticoagulant to prevent clotting. A whole blood sample normally comprises three major classes of blood cells including red blood cells (erythrocytes), white blood cells (leukocytes) and platelets (thrombocytes). Each class can be further divided into subclasses of members. For example, five major types or subclasses of white blood cells (WBCs) have different shapes and functions. White blood cells may include neutrophils, lymphocytes, monocytes, eosinophils, and basophils. There are also subclasses of the red blood cell types. The appearances of particles in a sample may differ according to pathological conditions, cell maturity and other causes. Red blood cell subclasses may include reticulocytes and nucleated red blood cells.

[0002] The enumeration of different classes and subclasses of cells can be an important tool for detecting pathological conditions such as various forms of infection. For example, the 5-part WBC Differential has for long been an invaluable test in the detection of hematological conditions. The 5-part Differential detects and enumerates the five major subtypes of WBC that are normally found in the peripheral blood, i.e., neutrophils, lymphocytes, monocytes, eosinophils and basophils. Other types of cell class and subclass identification can also be useful. For example, identification of cells in intermediate stages of maturation, such as early granulated cells, blast cells and / or band cells can also be useful in detection of various hematology disorders.

[0003] There are a variety of approaches used to support blood cell analysis such as enumeration of cell classes. For example, in some cases images may be captured of a sample which is mounted on a slide. However, there are drawbacks to known approaches. For example, slide imaging may be limited to only capturing images of a certain number of cells (e.g., the number of cells on a slide) and then presenting those images for review on a first in-first out basis. Other typesof approaches (e.g., fluorescence or light scatter measurements) are limited in that no images are captured. Accordingly, there is a need to improve blood cell analysis technology, particularly as it relates to analysis involving multiple cell classes and / or subclasses.SUMMARY

[0004] Described herein are methods and devices for determining how a user interface should be populated with cell images.

[0005] A first illustrative implementation of the technology described herein relates to a computer implemented method. Such a method may include receiving images of cells from the biological sample. In these images, there may be both first cell-type images and second cell-type images, where the second cell-type is a subset of the first cell-type. The method may also include establishing first and second cell type counts, and assigning a number of display images to the second cell-type. This number of display images may be based on a relationship of the second cell-type count to the first cell-type count.

[0006] A second illustrative implementation relates to a second computer implemented method. This type of method may include receiving images of cells from a biological sample, the images comprising abnormal cell-type images and normal cell-type images. Abnormal cell-type images may be identified from among the images of cells from the biological sample, and a subset of the images of cells from the biological sample may be displayed on a user display. In this type of method, the subset may include both abnormal cell-type images and normal cell- type images.

[0007] Systems, computer program products, and computer readable media corresponding to the above methods may also be implemented based on this disclosure.

[0008] While multiple examples are described herein, still other examples of the described subject matter will become apparent to those skilled in the art from the following detailed description and drawings, which show and describe illustrative examples of disclosed subject matter. As will be realized, the disclosed subject matter is capable of modifications in various aspects, allwithout departing from the spirit and scope of the described subject matter. Accordingly, the drawings and detailed description are to be regarded as illustrative in nature and not restrictive.BRIEF DESCRIPTION OF THE DRAWINGS

[0009] While the specification concludes with claims which particularly point out and distinctly claim the invention, it is believed the present invention will be better understood from the following description of certain examples taken in conjunction with the accompanying drawings, in which like reference numerals identify the same elements and in which:

[0010] FIG. 1 is a schematic illustration, partly in section and not to scale, showing operational aspects of an exemplary flowcell and high optical resolution imaging device for sample image analysis using digital image processing.

[0011] FIG. 2 illustrates a slide based vision inspection system.

[0012] FIG. 3 illustrates an exemplary user interface which may be presented in some implementations.

[0013] FIG. 4 illustrates an exemplary user interface which may be presented in some implementations.

[0014] FIG. 5 illustrates an exemplary user interface which may be presented in some implementations.

[0015] FIG. 6 illustrates a method which may be used in populating interfaces with cell images.

[0016] FIG. 7 illustrates a method which may be used in populating interfaces with cell images.

[0017] FIG. 8 illustrates a method that can be used to obtain repeatable results from pseudorandom shuffling.

[0018] FIG. 9 illustrates acts which may be performed to account for, and support highlighting of, abnormal cell types.

[0019] FIG. 10 illustrates an exemplary rounding-reallocation method which may be used in assigning display images to various encompassed cell types.

[0020] FIG. 11 illustrates a method which may be used in populating interfaces with cell images.

[0021] The drawings are not intended to be limiting in any way, and it is contemplated that various embodiments of the invention may be carried out in a variety of other ways, including those not necessarily depicted in the drawings. The accompanying drawings incorporated in and forming a part of the specification illustrate several aspects of the present invention, and together with the description serve to explain the principles of the invention; it being understood, however, that this invention is not limited to the precise arrangements shown.DETAILED DESCRIPTION

[0022] The present disclosure relates to apparatus, systems, compositions, and methods for analyzing a sample containing particles. In one embodiment, the disclosure relates to an automated particle imaging system which comprises an analyzer which may be, for example, a visual analyzer. In some embodiments, the visual analyzer may further comprise a processor to facilitate automated analysis of the images.

[0023] According to some aspects of this disclosure, a biological analyzer system comprising a visual analyzer may be provided for obtaining images of a sample comprising particles suspended in a liquid. Such a system may be useful, for example, in characterizing particles in biological fluids, such as detecting and quantifying erythrocytes, reticulocytes, nucleated red blood cells, platelets, and white blood cells, including white blood cell differential counting, categorization and subcategorization and analysis. Other similar uses such as characterizing blood cells from other fluids are also contemplated.

[0024] The analysis of blood cells in a blood sample is an exemplary application for which the subject matter is particularly well suited, though other types of body fluid samples may be used. For example, aspects of the disclosed technology may be used in analysis of a non-blood body fluid sample comprising blood cells (e.g., white blood cells and / or red blood cells), such asserum, bone marrow, lavage fluid, effusions, exudates, cerebrospinal fluid, pleural fluid, peritoneal fluid, and amniotic fluid. It is also possible that the sample can be a solid tissue sample, e.g., a biopsy sample that has been treated to produce a cell suspension. The sample may also be a suspension obtained from treating a fecal sample. A sample may also be a laboratory or production line sample comprising particles, such as a cell culture sample. The term sample may be used to refer to a sample obtained from a patient or laboratory or any fraction, portion or aliquot thereof. The sample can be diluted, divided into portions, or stained in some processes.

[0025] In some aspects, samples are presented, imaged and analyzed in an automated manner. In the case of blood samples, the sample may be substantially diluted with a suitable diluent or saline solution, which reduces the extent to which the view of some cells might be hidden by other cells in an undiluted or less-diluted sample. The cells can be treated with agents that enhance the contrast of some cell aspects, for example using permeabilizing agents to render cell membranes permeable, and histological stains to adhere in and to reveal features, such as granules and the nucleus. In some cases, it may be desirable to stain an aliquot of the sample for counting and characterizing particles which include reticulocytes, nucleated red blood cells, and platelets, and for white blood cell differential, characterization and analysis. In other cases, samples containing red blood cells may be diluted before introduction to the flow cell and / or imaging in the flow cell or otherwise.

[0026] The particulars of sample preparation apparatus and methods for sample dilution, permeabilizing and histological staining, generally may be accomplished using precision pumps and valves operated by one or more programmable controllers. Examples can be found in patents such as U.S. Pat. No. 7,319.907. Likewise, techniques for distinguishing among certain cell categories and / or subcategories by their attributes such as relative size and color can be found in U.S. Pat. No. 5,436,978 in connection with white blood cells. The disclosures of these patents are hereby incorporated by reference in their entirety.

[0027] Turning now to the drawings. FIG. 1 schematically shows an exemplary flow cell 22 for conveying a sample fluid through a viewing zone 23 of a high optical resolution imagingdevice 24 in a configuration for imaging microscopic particles in a sample flow stream 32 using digital image processing. Flow cell 22 is coupled to a source 25 of sample fluid which may have been subjected to processing, such as contact with a particle contrast agent composition and heating. Flow cell 22 is also coupled to one or more sources 27 of a particle and / or intracellular organelle alignment liquid (PIO AL), such as a clear glycerol solution having a viscosity that is greater than the viscosity of the sample fluid.

[0028] The sample fluid is injected through a flattened opening at a distal end 28 of a sample feed tube 29, and into the interior of the flow cell 22 at a point where the PIOAL flow has been substantially established resulting in a stable and symmetric laminar flow of the PIOAL around / surrounding (e.g., circumferentially in a circular cross-sectional arrangement, or surrounding a plurality of sides of in a non-circular (e.g., rectangular) cross-sectional arrangement) the ribbon-shaped sample stream. The sample and PIOAL streams may be supplied by precision metering pumps that move the PIOAL with the injected sample fluid along a flowpath that narrows substantially. The PIOAL envelopes and compresses the sample fluid in the zone 21 where the flowpath narrows. Hence, the decrease in flowpath thickness at zone 21 can contribute to a geometric focusing of the sample stream 32. The sample fluid ribbon 32 is enveloped and carried along with the PIOAL downstream of the narrowing zone 21, passing in front of, or otherwise through the viewing zone 23 of, the high optical resolution imaging device 24 where images are collected, for example, using a CCD 48. In this way, flow imaging is performed where images from the flowing sample stream and the cellular material contained therein are collected. Processor 18 can receive, as input, pixel data from CCD 48. The sample fluid ribbon flows together with the PIOAL to a discharge 33.

[0029] As shown here, the narrowing zone 21 can have a proximal flowpath portion 21a having a proximal thickness PT and a distal flowpath portion 21b having a distal thickness DT, such that distal thickness DT is less than proximal thickness PT. The sample fluid can therefore be injected through the distal end 28 of sample tube 29 at a location that is distal to the proximal portion 21a and proximal to the distal portion 21b. Hence, the sample fluid can enter the PIOAL envelope as the PIOAL stream is compressed by the zone 21. wherein the sample fluidinjection tube has a distal exit port through which sample fluid is injected into flowing sheath fluid, the distal exit port bounded by the decrease in flowpath size of the flow cell.

[0030] The digital high optical resolution imaging device 24 with objective lens 46 is directed along an optical axis that intersects the ribbon-shaped sample stream 32. The relative distance between the objective 46 and the flow cell 33 is variable by operation of a motor drive 54, for resolving and collecting a focused digitized image on a photosensor array. Additional information regarding the construction and operation of an exemplary flow cell such as shown in FIG. 1 is provided in U.S. Patent 9,322,752, entitled “Flow cell Systems and Methods for Particle Analysis in Blood Samples,” filed on March 17, 2014, the disclosure of which is hereby incorporated by reference in its entirety. Descriptions of approaches which may be used for focusing in an imaging system such as shown in FIG. 1 are provided in Published App. No.2024 / 0357232 titled “Focus Quality Determination through Multi-Layer Processing,” filed on June 11, 2024, U.S. Patent 9,857,361 titled “Flowcell, Sheath fluid, and Autofocus Systems and Methods for Particle Analysis in Urine Samples”, filed on March 17, 2014, U.S. Patent 10,705,008 titled “Autofocus systems and methods for particle analysis in blood samples”, filed on March 17, 2014, U.S. patent 10,705,011, titled “Dynamic Focus System and Methods”, filed October 5, 2017, and international application W02023 / 150064 titled “Measure Image Quality of Blood Cell Images”, filed January 27, 2023, the disclosures of each of which are hereby incorporated by reference in their entirety.

[0031] Aspects of the disclosed technology may also be applied in contexts other than flow cell systems such as shown in FIG. 1. For instance, some embodiments of biological image analysis can utilize different analytical techniques which render large numbers of images (e.g., not limited to flow imaging techniques). Some examples include full-field slide imaging, or slide imaging beyond a small region of interest - these techniques involve imaging an entire slide or a large region of a slide to obtain a large number of blood cell images. An illustrative slide based vision inspection system 200 which may be used for these types of implementations is shown in FIG. 2. In the system shown in FIG. 2, a slide 202 comprising a sample, such as a blood sample, is placed in a slide holder 204. The slide holder 204 may be adapted to hold anumber of slides or only one, as illustrated in FIG. 2. An image capturing device 206, comprising an optical system 208 and an image sensor 210, is adapted to capture image data depicting the sample in the slide 202.

[0032] The image data captured by the image capturing device 206 can be transferred to an image processing device 212. The image processing device 112 may be an external apparatus, such as a personal computer, connected to the image capturing device 206. Alternatively, the image processing device 212 may be incorporated in the image capturing device 206. The image processing device 212 can comprise a processor 214, associated with a memory 216, configured to determine changes needed to determine differences between the actual focus and a correct focus for the image capturing device 206. When the difference is determined an instruction can be transferred to a steering motor system 218. The steering motor system 218 can, based upon the instruction from the image processing device 212, alter the distance z between the slide 202 and the optical system 208. Descriptions of approaches which may be used for focusing using this type of setup are provided in U.S. Published App. No.2024 / 0357232 titled “Focus Quality Determination through Multi-Layer Processing,” filed on June 11, 2024, U.S. Patent 9,857,361 titled “Flowcell, Sheath fluid, and Autofocus Systems and Methods for Particle Analysis in Urine Samples”, filed on March 17, 2014, U.S. Patent 10,705,008 titled “Autofocus systems and methods for particle analysis in blood samples”, filed on March 17, 2014, U.S. patent 10,705,011 , titled “Dynamic Focus System and Methods”, filed October 5, 2017, and international application W02023 / 150064 titled “Measure Image Quality of Blood Cell Images”, filed January 27, 2023, the disclosures of each of which are hereby incorporated by reference in their entirety.

[0033] In some examples of the visual analyzer embodiments contemplated herein, a large amount (e.g., 5,000-100,000, 40,000-60,000 images, or about 50,000 images, or other numbers of images) may be collected in one sample, e.g., by high optical resolution imaging device 24 of FIG. 1. For instance, where flow imaging techniques are used, the system can generally display all or a significant subset of whatever is captured by the camera - therefore a large number of cells are imaged and capable of being displayed.

[0034] Once cell images have been captured (e.g., in a system such as illustrated in FIGS. 1-2), they may be presented to a user via one or more user interfaces, such as those shown in FIGS. 3-5. Turning first to FIG. 3, that figure illustrates an example user interface 400 for displaying selected cells to a user. User interface 400 can allow a user to toggle amongst tabs 410 for different pages of cell types (WBCs, RBCs, platelets, etc.).

[0035] Each tab 410 shows the selected images 430 of the cells associated with that tab’s cell types (i.e., WBC tab shows white blood cell images, RBC tab shows red blood cell images, platelet tab shows platelet images). In some examples, the images 430 associated with each tab are arranged in respective groupings 420 for each cell type (e.g., for white blood cells - groupings 420 including cell types - neutrophils, eosinophils, etc.). In various embodiments, particular cell types can be grouped into groupings 420 (e.g., at least WBC’s, but not necessarily RBC’s or platelets) - in other embodiments no groupings 420 are used for any cell type, in other embodiments all cell types are arranged into groupings 420 (e.g., all of WBC. RBC, platelets are respectively arranged into respective groupings 420 for each cell type).

[0036] How many images 430 are shown for each grouping 420 on a particular page of the interface of FIG. 3 can be determined e.g., by a predetermined number, user preference, or in other manners. Additional pages of cell images can be seen by e.g., selecting button 450. For example, if the user wants to see the next page of a certain cell type, e.g. WBCs or certain groupings of WBCs such as neutrophils, more images can be retrieved from the total set of cell images and displayed on the user interface 400. Certain embodiments can display cell images to a user in user interface 400 in any desired number. For example, a first page of user interface 400 could show 200 WBC images, apportioned into different groupings 420, and 200 RBC images under the RBC tab 410. and so forth. But other embodiments could use 100 images on a first page, or 300, or any other desired number. Other embodiments can display all the cells associated with each particular cell type (i.e., whatever cells are captured by the cell imaging system are displayed to the user for each cell type). Alternatively, some cell types can display all cells and other cell types have a numeric cutoff - by way of example, particular cell types may be more indicative of patient health (and less numerous) where display of all cells may beuseful in a patient health assessment where it would be desirable to show all such cell types (e.g., there may be value in showing all WBCs compared to RBCs since there are less WBCs in each patient blood sample and this may be more indicative of immune health).

[0037] User interface 400 can be divided into tabs 410, with each tab showing selected images 430 selected and apportioned into groupings 420 by e.g., other methods described herein. Other embodiments could only have one tab 410, showing groupings 420 of RBC, WBC, platelets, etc., and sub-groupings (neutrophils, eosinophils, etc.), where the number of images for RBC, WBC, neutrophils, etc. can be chosen by e.g., user preference, predetermined numbers, or other approaches. One such embodiment is illustrated in UI 600 of FIG. 4. Here, user interface 600 shows groupings 620, such as WBC neutrophils, platelets-giant, etc. Selected images 630 are shown for each grouping 620. More pages can be available via button 650.

[0038] In some embodiments, and in reference to FIG. 3. each tab 410 could show a different number of selected images. For example, a method could be performed to image or analyze a biological sample and select which images to display to a user. Such a method (such as described in various embodiments herein) could be run separately for WBCs, RBCs and platelets. The method could be configured to provide selected images 430 for WBCs, RBC’s and platelets. In some examples, the number of cell images displayed for each cell type can be different for various cell types (e.g., 200 selected images for WBC’s, 400 selected images for RBC, 400 selected images for platelets, and 100 selected images for nucleated red blood cells).

[0039] FIG. 5 illustrates one embodiment of another UI 700 with results of blood sample testing, which may include various parameters and / or review indications and corresponding descriptions derived from the images and / or other data related to the sample. In the UI 700 the user is presented with a worklist 701 comprising a set of review indications 702 and descriptions 703 of those review indications 702. The UI 700 of FIG. 5 also provides the user with categorizations for the different review indications (e.g., “Sample Quality” and “Morphology Message”) and brief instructions for the types of review and / or other remedial actions which may be appropriate in light of the review indications which are displayed. To assist with this review, user interface 700 shows second sets 710 of cell images 704corresponding to selected images which can be obtained from a first set (the first set being the total set of images obtained by an imaging or analysis process). Cell images 704 could comprise, for example, the sets of selected images 430, 630 described with respect e.g., FIG.3 and FIG. 4. When a thumbnail image is clicked on or otherwise selected, a full resolution copy of the image corresponding to the selected thumbnail could be displayed so that the user could perform additional review tasks.

[0040] Turning next to FIG. 6, that figure illustrates a method which may be used in populating interfaces with cell images (e.g., interfaces such as shown in FIGS. 3-5). As shown in FIG. 6, such a method may include receiving 301 images of cells. These images of cells may be images captured from a biological sample (e.g., using systems such as shown in FIGS. 1-2), and may include multiple cell types. For example, in some cases, the images of cells from the biological sample may include first cell-type images and second cell-type images where the second celltype is a subset of the first cell-type (e.g., the first cell-type is white blood cells, and the second cell-type is at least neutrophils, lymphocytes, monocytes, eosinophils or basophils - which are subsets or subtypes of white blood cells).

[0041] Populating interfaces with cell images may also include establishing 302 first and second celltype counts and assigning 303 a number of display images to the second cell type. For example, the first cell-type count may be a white blood cell (WBC) count, with the second cell-type count being a count of neutrophils (or lymphocytes, or monocytes, or some other subset of white blood cells), and the number of display images assigned to the second cell type may be a number based on the number of neutrophils (or other applicable subset of white blood cells) within the WBC count. To illustrate, consider a case in which approximately 50,000 cell images are collected, and a method such as shown in FIG. 6 is used to determine how many of those images should be displayed on an initial portion (e.g., a first page) of a user interface for each of a variety of cell types identified in the sample. In this type of case, cell type counts for various cell types may be established by classifying imaged cells (e.g., based on morphology, such as using techniques described in international patent application PCT / US23 / 85666, filed December 22, 2023 and titled “Multi-level image classifier for blood cell images” and / orintemational patent application PCT / US23 / 85714, filed December 22, 2023 for “Population based cell classification,” the disclosures of which are hereby incorporated by reference in their entirety) into various classes (e.g., WBCs, RBCs, platelets) and subclasses (e.g., neutrophils, eosinophils, etc.). With these counts, a portion of a number of cell images for a particular type of cell can then be assigned to types which represent subsets of that type. For example, if a first page of a user interface is configured to display 200 WBC images, and the count of neutrophils accounts for half of the WBC count, then 100 of the WBC images to display on the first page could be neutrophils.

[0042] This approach can also be applied to the display of greater numbers of images, and to greater numbers of types of cells. For instance, in the example described above, if 30% of the WBCs in a sample were lymphocytes, then in addition to displaying 100 images of neutrophils (reflecting the fact that neutrophils were 50% of the WBCs), the first page of the user interface might also display 60 images of lymphocytes (reflecting that 30% of 200 images is 60 images). Additional while blood cell types could also be displayed, with the number of pictures for each white blood cell type corresponding to the percentage of white blood cells represented by that type. For instance, in a five part differential, 40-60% of a patient’s white blood cells will normally be neutrophils, 20-40% of a patient’s white blood cells will normally be lymphocytes, 2-8% of a patient’s white blood cells will normally be monocytes, 1-4% of a patient’s white blood cells will normally be eosinophils, and 0.5-1% of a patient’s while blood cells will normally be basophils. Thus, in some implementations of the disclosed technology, of the images on the front page of a user interface allocated to white blood cells, 40-60% of those images can be expected to be allocated to neutrophils, 20-40% of those images can be expected to be allocated to lymphocytes, 2-8% of those images can be expected to be allocated to monocytes. 1-4% of those images can be expected to be allocated to eosinophils, and 0.5-1% of those images can be expected be allocated to basophils, with the specific percentages allocated to the particular types of white blood cells varying depending on the composition of the patient’s sample. Concretely, if an interface displayed 200 WBC images on its first page, then table 1, below, illustrates how those images may be assigned based on particular percentages for white blood cell types.Table 1: Exemplary allocation of images on an interface’s first page for particular percentages. It should be understood that the percentages and allocations set forth in the above table are illustrative only, and that they should not be treated as limiting.Other white blood cell types, such as immature granulocytes (e.g., promyelocytes, myelocytes, and metamyelocytes) may also be identified and have images assigned to them in some implementations. Similarly, in some cases, there may be a specific “other” category which may include while blood cells which cannot be categorized into one of the types reported in that implementation (e.g., the types listed in table 1). Thus, regardless of the way white blood cells (or other types of cells) are categorized, implementations of the disclosed technology may allow the initial portion of a user interface to provide the user with proportionate representations of the various cell types in the sample, even if certain classes or subclasses may be over- or under- represen ted in the initially captured cell images.

[0043] Other acts may also be included in a method such as shown in FIG. 6. To illustrate, consider FIG. 7, which illustrates that a method such as shown in FIG. 6 may also include acts such as receiving 501 and generating a seed for a sample. This may be done by, for instance, when a cassette containing a sample is inserted into an analyzer, automatically generating an activity ID which is unique to the sample but which is blind to identifying patient information (e.g., a numeric value created by concatenating the time the cassette was inserted into the analyzer with the position of the sample in the cassette), and then running that activity ID through a hashing function to create a unique seed for the sample. Subsequently (e.g., after assigning 303 a number of display images to one or more cell types, after establishing 302 cell type counts but before assigning 303 a number of display images, etc.) this seed can be used in determining 502 a display order with pseudorandom shuffling to create a mapping between the order in which the cell images were captured and an order in which they should be displayed. Further,when the pseudorandom shuffling is implemented such that using the same seed always creates the same mapping, if the display order is subsequently redetermined 503 (if the sample is rerun for validation), then the use of this pseudorandom shuffling plus sample level seed can guarantee that the results of the redetermines 503 will be the same as the results from the display order being originally determined 502.

[0044] Turning next to FIG. 8, that figure illustrates a method that can be used to obtain repeatable results from pseudorandom shuffling. As shown in FIG. 8. such a method may begin with initializing 801 an array defining the order of images after shuffling, and initializing 802 a counter which could control the shuffling process. These initialization steps may be done by allocating memory for an array of numbers having a length equal to the number of received cell images, then populating it with consecutive ascending integer values (e.g., 1, 2, 3, ... to n, where n is the number of images), and setting the counter to the index of a first value in the array (e.g., 0). The shuffling process may then proceed with generating 803 a random index. This may be done by using a pseudorandom number generator such as the Mersenne twister (described in Makato Matsumoto and Takuji Nishimura, Mersenne Twister: A 623- Dimensinally Equidistributed Uniform Pseudo-Random Number Generator, available at https : / / dl .acm . org / doi / pdff 10.1.145 / 272991.272995 , and incorporated by reference in its entirety), which has the property that it will always generate the same sequence of numbers when provided with the same seed, to generate an index which is random while still being repeatable as well as meaningful in the context of the shuffling algorithm (e.g., the index may be a pseudorandom number which is greater than the counter, but less than the index of the final number in the array).

[0045] In the method of FIG. 8, once the random index is generated 803. the elements in the array at the index represented by the counter and the random index may be swapped 804. For example, if the array is made up of elements a[0] to a[n-l], the counter is represented by variable i, and the random index is represented by variable j, then after swapping 804, the value that had been assigned to element a[i] would be assigned to element a[j], and the value that had been assigned to element a j] would now be assigned to element a[i]. After the elements are swapped, a test805 could be run to determine if a stopping condition (e.g., the counter had reached some predetermined value, such as half the number of elements in the array) was satisfied. If the stopping condition had not been satisfied, then the process could continue with updating 806 the counter (e.g., incrementing it) and generating 803 and swapping 804 a new random index. Otherwise, the process could terminate 807, and the swapped values of the array would define the display order for each class and subclass of cells in the sample. For instance, to determine the order in which to display 100 neutrophils, a computer program could iterate through an array generated as described in the context of FIG. 8 and, each time it encountered an array element whose value corresponded to a neutrophil (e.g., the value in the array corresponded to the index of a cell identified as a neutrophil in an array which stored cell images in the order in which they were received) the image of that neutrophil could be appended to a set of neutrophil images to display, until either 100 neutrophil images had been added or there were no more elements in the array.

[0046] Another example of actions that may be incorporated into a method such as shown in FIG. 6 is provided in FIG. 9, which illustrates acts which may be performed to account for, and support highlighting of, abnormal cell types. This method may be performed, for example, in the context of an analyzer which was configured to be able to identify if one or more images for a biological sample depicted abnormal cells (e.g., cells which would not ordinarily be present in a biological sample absent some kind of underlying pathology, such as blastocytes, giant platelets, platelet clumps, atypical lymphocytes, etc.). In such a context, after the imaged cells had been identified, a determination 901 could be made as to whether one of the abnormal cel types should be highlighted (e.g., including one or more images depicting that type of cell on an initial portion of a user interface and / or adding those images to a separate review list which was visually set off through color or other the use of a marker, etc.). This determination 901 may be made, for example, by comparing the proportion of cells of the abnormal cell type to a threshold (e.g., 0.5%) and, if the proportion of abnormal cells was above the threshold, highlighting at least one image of depicting that abnormal cell type (e.g., displaying at least one image depicting that abnormal cell on a first page of a user interface). This determination 901 could then be repeated for each abnormal cell type the analyzer was configured torecognize until all abnormal cell types to highlight had been identified. In some examples, the abnormal cell type threshold can be a percentage (e.g., 0.5%) or a particular number of abnormal cells (e.g.. three). The abnormal cell type thresholds can vary for various cell types, and can be pre-set within the software or can be customizable by the user.

[0047] Once all abnormal cell types to highlight had been identified, the number of images to display in the context of that highlighting could be used to determine 902 a number of display images for a normal cell type (e.g., a type of cells which would normally be expected to be present in a biological sample, such as WBCs). To illustrate how this may be done, consider a case such as discussed above in the context of FIG. 3 in which a user interface was configured to display 200 WBC images on its first page. In such a case, if it was determined that two images of abnormal cell types (e.g., an image of an atypical lymphocyte and an image of an atypical monocyte) should be displayed on the first page, then the number of non-abnormal WBC images to be displayed on the first page could be reduced from 200 to 198. This number of display images could then be used in assigning 303 a number of display images to a cell type (referred to in this context as the “included” cell type) representing a subset of the cells of the cell type (referred to in this context as the “encompassing” cell type) for which the number of display images which had just been determined 902. For example, a portion of the number of display images for the encompassing cell type could be assigned to the included cell type such that the ratio of the number of display images for the encompassing cell type to the number of display images for the included cell type would be the same as the ratio of the cell type count for the encompassing cell type to the cell type count for the included cell type. Other approaches are also possible, including, but not limited to, rounding-reallocation approaches such as described below in the context of FIG. 10.

[0048] Turning now to FIG. 10, that figure illustrates an exemplary rounding-reallocation method which may be used in assigning display images to various encompassed cell types. In this document, a rounding-reallocation method should be understood as a method in which display images are allocated via division and rounding which is repeated for as long as the allocation does not result in a predetermined number of display images (e.g.. if 198 WBC images shouldbe displayed on the first page of a user interface, but the allocation only results in 197 display images across all WBC subtypes, then the division and rounding could be repeated, such as with a new divisor being used to address the discrepancy). In FIG. 10, the illustrative roundingreallocation method begins with generating 1001 a divisor. This is the divisor which will be used initially when allocating display images, and may be generated by dividing the cell type count for the encompassing cell type by the number of display images (e.g., by dividing the WBC count by the number of images to display on the first page of the user interface after accounting for any abnormal cell type images to be highlighted through being displayed on that page). This divisor is then used to create 1002 real number allocations for each included cell type. To continue the example of white blood cells, the real number allocations could be the neutrophil count divided by the divisor, the lymphocyte count divided by the divisor, the monocyte count divided by the divisor, etc. These real number allocations are then uniformly rounded 1003 (e.g., all rounded up or all rounded down) to create whole number allocations.

[0049] In the method of FIG. 10, once the whole number allocations have been created 1003, those allocations can be summed 1004 (i.e., added together) and that sum can be compared 1005 to the target number of display images (e.g., the number of display images for all WBCs). If the numbers are the same, then the whole number allocations can be used 1006 as the allocations of display images to the included subtypes. Otherwise, if the sum of the whole number allocations did not equal the target, then the divisor could be updated 1007 (e.g., if the sum was greater than the target, then the divisor could be increased, while if the sum was less than the target then the divisor could be decreased). The updated divisor could then be used to create 1002 a new set of real number allocations, and the rounding and reallocation process could continue until a sum of whole number allocations which equaled the target number of display images was obtained.

[0050] It should be understood that, while FIG. 10 illustrated an approach to assigning display images to included cell types via rounding-reallocation, that figure, as well as the approach it described, is intended to be illustrative only. Additional information on potential approaches which could be used in assigning display images can be found in, for example, “The JeffersonMethod of Apportionment,” M. L. Balinski and H. P. Young, SIAM Review Vol. 20, No. 2, pp. 278-284 (1978) or “Comparing Proportional Representation Electoral Systems: Quotas, Thresholds, Paradoxes and Majorities,” British Journal of Political Science, 22(4):469-496 (1992), each of which is incorporated herein by reference in its entirety. Accordingly, the above discussion of assigning display images should be understood as being illustrative only, and should not be treated as limiting.

[0051] Variations on potential implementations of the disclosed technology are also possible in aspects other than approaches to assigning display images. For example, while the discussion above separately addressed generating and using a seed for redetermining a display order, and assigning numbers of display images based on proportions separately in the context of FIGS.7 and 9, it is possible that some implementations of the disclosed technology may combine these functionalities, such as in a method in which, after images are received 301 and cell type counts are established 302, a new display order can be created using pseudorandom shuffling as described in the context of FIG. 7, a determination of abnormal cell type images to highlight could be made as described in the context of FIG. 9, and then the remaining display images for the first page of an interface could be populated using the new display order, thereby combining the approaches described separately above in the context of FIGS. 7 and 9.

[0052] As another example of a type of variation in how the disclosed technology may be applied, it is also possible that some implementations may not include the acts described previously, such as those illustrated in the method of FIG. 6. For example, in some cases, the disclosed technology may be used to implement a method such as shown in FIG. 11. In that method, initially, images of cells from a biological sample would be received 301 in a manner similar to that described above for FIG. 6. However, in the method of FIG. 11, this would be followed by identifying 1102 abnormal cell type images in the images of cells from the biological sample. This may be done, for example, by using a neural network which had been trained on a data set of annotated cell images which included some images that had been annotated as having abnormal cell types (e.g., blastocysts, atypical lymphocytes, giant platelets, platelet clumps, RBC inclusions and malformed RBCs). With these abnormal cell type imagesidentified 1102, a subset of the images of cells from the biological sample may be displayed 1103 on a user display, with the subset comprising both abnormal cell type images and normal cell type images (e.g., images of neutrophils, lymphocytes, monocytes, eosinophils basophils, red blood cells and / or platelets). Of course, a system implementing a method such as shown in FIG. 11 can also include functionality described previously in the context of FIGS. 6-10. For example, such a system may determine what abnormal cell type images should be displayed 1102 based on their prevalence relative to a threshold as described previously for determining 901 whether to highlight an abnormal cell type in the context of FIG. 9. Similarly, the order in which to display 1103 the normal cell type images from the subset of cell images, and how the normal cell type images should be allocated within the subset of cell images could be determined using pseudorandom shuffling and rounding reallocation methods as described previously in the context of FIGS. 7-8 and 9-10. Accordingly, the discussion of FIG. 11 as an alternative to methods including actions such as shown in FIG. 6 should be understood as being illustrative only, and should not be treated as limiting.

[0053] As a further illustration of potential implementations and applications of the disclosed technology, the following examples are provided of non-exhaustive ways in which the teachings herein may be combined or applied. It should be understood that the following examples are not intended to restrict the coverage of any claims that may be presented at any time in this application or in subsequent filings of this application. No disclaimer is intended. The following examples are being provided for nothing more than merely illustrative purposes. It is contemplated that the various teachings herein may be arranged and applied in numerous other ways. It is also contemplated that some variations may omit certain features referred to in the below examples. Therefore, none of the aspects or features referred to below should be deemed critical unless otherwise explicitly indicated as such at a later date by the inventors or by a successor in interest to the inventors. If any claims are presented in this application or in subsequent filings related to this application that include additional features beyond those referred to below, those additional features shall not be presumed to have been added for any reason relating to patentability.

[0054] Example 1

[0055] A computer implemented method comprising: receiving images of cells from a biological sample, the images including first cell-type images, and second cell-type images; establishing a first cell-type count, and a second cell-type count from the images, wherein the second cell- type is a subset of the first-cell-type; and assigning a number of display images to the second cell-type, wherein the number of display images assigned to the second cell-type is based on a relationship of the second cell-type count to the first cell-type count.

[0056] Example 2

[0057] The method of example 1, wherein the first cell-type is white blood cells and the second cell- type is selected from neutrophils, lymphocytes, monocytes, eosinophils, and basophils.

[0058] Example 3

[0059] The method of any of examples 1 to 2, wherein the method further comprises determining a display order for a set of the second cell-type images, wherein the display order is different from an order in which the set of the second cell-type images were received.

[0060] Example 4

[0061] The method of example 3, wherein determining the display order for the set of the second celltype images comprises: determining a display order for the first cell-type images by shuffling the first cell-type images out of the order in which they were received using pseudorandom shuffling, wherein the pseudorandom shuffling provides the display order for the first cell-type images based on a seed for the biological sample; and extracting the display order for the set of the second cell-type images from the display order for the first cell-type images.

[0062] Example 5

[0063] The method of example 4, wherein: the method comprises redetermining the display order for the first cell-type images using the pseudorandom shuffling and the seed for the biologicalsample; and the display order from determining the display order for the first cell-type images is the same as the display order from redetermining the display order for first cell-type images.

[0064] Example 6

[0065] The method of any of examples 4-5. wherein the seed for the biological sample is a seed generated for the sample upon receipt of the biological sample.

[0066] Example 7

[0067] The method of any of examples 1-6. wherein the method comprises, for each of a set of abnormal cell-types, determining whether to highlight that abnormal cell-type by displaying a number of images of cells from the biological sample having that abnormal cell-type on an initial portion of a user interface.

[0068] Example 8

[0069] The method of example 7, wherein: the method comprises determining a number of display images for the first cell-type based on, for each abnormal cell-type from the set of abnormal cell-types determined to be highlighted, reducing the number of display images for the first cell-type from an initial number based on the number of images of cells having that abnormal cell-type displayed on the initial portion of the user interface; and assigning the number of display images to the second cell-type comprises assigning a portion of the number of display images for the first cell-type to the second cell-type based on a proportion of the second celltype in the first cell-type

[0070] Example 9

[0071] The method of any of examples 1-8, wherein assigning the number of display images to the second cell-type is performed using a rounding-reallocation method.

[0072] Example 10

[0073] The method of any of examples 1-9, wherein receiving images of cells from the biological sample comprises: establishing a flow of alignment fluid from an alignment fluid reservoir into a flowcell; creating a sample stream comprising the blood sample and a sheath of alignment fluid surrounding the sample fluid based on injecting the sample fluid from a channel into the flow of alignment fluid; and using a camera focused on a viewing area of the flowcell to capture a plurality of images as the sample stream is flowing through the viewing area of the flowcell.

[0074] Example 11

[0075] A computer implemented method comprising: receiving images of cells from a biological sample, the images comprising abnormal cell-type images and normal cell-type images; identifying abnormal cell-type images from among the images of cells from the biological sample; and displaying a subset of the images of cells from the biological sample on a user display, wherein the subset includes both abnormal cell-type images and normal cell-type images.

[0076] Example 12

[0077] The method of example 11, wherein: the abnormal cell-type images comprise images of one or more of blastocysts, atypical lymphocytes, giant platelets, platelet clumps, RBC inclusions, and malformed RBCs; and the normal cell-types comprise images of one or more of neutrophils, lymphocytes, monocytes, eosinophils, basophils, red blood cells and platelets.

[0078] Example 13

[0079] The method of any of examples 11-12, wherein: the abnormal cell-type images comprise abnormal cell-type images depicting cells of a first abnormal cell-type; the subset of the images of cells from the biological sample comprises one or more images depicting cells of the first abnormal cell-type; displaying the subset of the images of cells from the biological sample comprises displaying the subset of images of cells from the biological sample on an initial portion of a user interface; and the method comprises, prior to displaying the subset of the images of cells from the biological sample, determining whether to display one or more imagesdepicting cells of the first abnormal cell-type based on whether a prevalence of cells of the first abnormal cell-type exceeds a threshold.

[0080] Example 14

[0081] The method of any of examples 11-13, wherein the method further comprises determining a display order for the normal cell-type images in the subset of images of cells, wherein the display order is different from an order in which the normal cell-type images were received.

[0082] Example 15

[0083] The method of example 14, wherein determining the display order for the normal cell-type images in the subset of cell images comprises: determining a display order for the normal cell- type images in the images of cells from the biological sample by shuffling the normal cell-type images of cells from the biological sample out of an order in which they were received using pseudorandom shuffling, wherein the pseudorandom shuffling provides the display order for the normal cell-type images in the images of cels from the biological sample based on a seed for the biological sample; and extracting the display order for the normal cell-types images in the subset of cell images from the display order for the normal cell-type images in the images of cells from the biological sample.

[0084] Example 16

[0085] The method of example 15, wherein: the method comprises redetermining the display order for the normal cell-type images in the images of cells from the biological sample using the pseudorandom shuffling and the seed for the biological sample; and the display order from determining the display order for the normal cell-type images in the images of cells from the biological sample is the same as the display order from redetermining the display order for the normal cell-type images in the images of cells from the biological sample.

[0086] Example 17

[0087] The method of any of examples 15-16, wherein the seed for the biological sample is a seed generated for the biological sample upon receipt of the biological sample.

[0088] Example 18

[0089] The method of any of examples 11-17. wherein the method comprises determining the number of normal cell-type images to include in the subset of images of cells from the biological sample based on, for each abnormal cell-type image in the subset of image of cells from the biological sample, reducing the number of images of normal cell-type images from an initial number.

[0090] Example 19

[0091] The method of any of examples 11-18, wherein: the normal cell-type images included in the images of cells from the biological sample comprise images of a plurality of cell-types; and the method comprises determining how may images of normal cell-type images for each of the plurality of cell-types to include in the subset of the images of cells from the biological sample using a rounding-reallocation method.

[0092] Example 20

[0093] The method of any of examples 11-19, wherein receiving images of cells from the biological sample comprises: establishing a flow of alignment fluid from an alignment fluid reservoir into a flowcell; creating a sample stream comprising the blood sample and a sheath of alignment fluid surrounding the sample fluid based on injecting the sample fluid from a channel into the flow of alignment fluid; and using a camera focused on a viewing area of the flowcell to capture a plurality of images as the sample stream is flowing through the viewing area of the flowcell.

[0094] Example 21

[0095] A system, comprising: one or more processors; and a non-transitory computer readable medium having stored thereon instructions operable to, when executed by the one or more processors, perform the method of any preceding example.

[0096] Example 22

[0097] A non-transitory computer-readable storage medium comprising instructions which, when executed by a computer, cause the computer to carry out the steps of any one of the methods of examples 1 to 20.

[0098] Example 23

[0099] A computer program product comprising instructions which, when the program is executed by a computer, cause the computer to carry out the steps of any one of the methods of examples 1 to 20.

[0100] Example 24

[0101] A system comprising: one or more processors; one or more non-transitory computer readable mediums storing instructions to, when executed, perform a set of acts comprising: receiving images of cells from a biological sample, the images including first cell-type images, and second cell-type images; establishing a first cell-type count, and a second cell-type count from the images, wherein the second cell-type is a subset of the first-cell-type; and assigning a number of display images to the second cell-type, wherein the number of display images assigned to the second cell-type is based on a relationship of the second cell-type count to the first cell-type count.

[0102] Example 25

[0103] The system of example 24, wherein the first cell-type is white blood cells and the second cell-type is selected from neutrophils, lymphocytes, monocytes, eosinophils, and basophils.

[0104] Example 26

[0105] The system of any of examples 24-25, wherein the set of acts further comprises determining a display order for a set of the second cell-type images, wherein the display order is different from an order in which the set of the second cell-type images were received.

[0106] Example 27

[0107] The system of example 26, wherein determining the display order for the set of the second cell-type images comprises: determining a display order for the first cell-type images by shuffling the first cell-type images out of the order in which they were received using pseudorandom shuffling, wherein the pseudorandom shuffling provides the display order for the first cell-type images based on a seed for the biological sample; and extracting the display order for the set of the second cell-type images from the display order for the first cell-type images.

[0108] Example 28

[0109] The system of example 27, wherein: the set of acts comprises redetermining the display order for the first cell-type images using the pseudorandom shuffling and the seed for the biological sample: and the display order from determining the display order for the first cell-type images is the same as the display order from redetermining the display order for first cell-type images.

[0110] Example 29

[0111] The system of any of examples 27-28, wherein the seed for the biological sample is a seed the system is configured to generate for the sample upon receipt of the biological sample.

[0112] Example 30

[0113] The system of any of examples 24-29, wherein the set of acts comprises, for each of a set of abnormal cell-types, determining whether to highlight that abnormal cell-type bydisplaying a number of images of cells from the biological sample having that abnormal celltype on an initial portion of a user interface.

[0114] Example 31

[0115] The system of example 30, wherein: the set of acts comprises determining a number of display images for the first cell-type based on, for each abnormal cell-type from the set of abnormal cell-types determined to be highlighted, reducing the number of display images for the first cell-type from an initial number based on the number of images of cells having that abnormal cell-type displayed on the initial portion of the user interface; and assigning the number of display images to the second cell-type comprises assigning a portion of the number of display images for the first cell-type to the second cell-type based on a proportion of the second cell-type in the first cell-type.

[0116] Example 32

[0117] The system of any of examples 24-31, wherein the system is configured to assign the number of display images to the second cell-type using a rounding-reallocation method.

[0118] Example 33

[0119] The system of any of examples 24-32, wherein: the system comprises a biological analyzer comprising a flowcell and a camera; and receiving images of cells from the biological sample comprises: establishing a flow of alignment fluid from an alignment fluid reservoir into the flowcell; creating a sample stream comprising the blood sample and a sheath of alignment fluid surrounding the sample fluid based on injecting the sample fluid from a channel into the flow of alignment fluid; and using the camera to capture a plurality of images as the sample stream is flowing through a viewing area of the flowcell.

[0120] Example 34

[0121] A system comprising: one or more processors; one or more non-transitory computer readable mediums storing instructions to. when executed, perform a set of acts comprising:receiving images of cells from a biological sample, the images comprising abnormal cell-type images and normal cell-type images; identifying abnormal cell-type images from among the images of cells from the biological sample; and displaying a subset of the images of cells from the biological sample on a user display, wherein the subset includes both abnormal cell-type images and normal cell-type images.

[0122] Example 35

[0123] The system of example 34, wherein: the abnormal cell-type images comprise images of one or more of blastocysts, atypical lymphocytes, giant platelets, platelet clumps, RBC inclusions, and malformed RBCs; and the normal cell-types comprise images of one or more of neutrophils, lymphocytes, monocytes, eosinophils, basophils, red blood cells and platelets.

[0124] Example 36

[0125] The system of any of examples 34-35, wherein: the abnormal cell-type images comprise abnormal cell-type images depicting cells of a first abnormal cell-type; the subset of the images of cells from the biological sample comprises one or more images depicting cells of the first abnormal cell-type; displaying the subset of the images of cells from the biological sample comprises displaying the subset of images of cells from the biological sample on an initial portion of a user interface; and the set of acts comprises, prior to displaying the subset of the images of cells from the biological sample, determining whether to display one or more images depicting cells of the first abnormal cell-type based on whether a prevalence of cells of the first abnormal cell-type exceeds a threshold

[0126] Example 37

[0127] The system of any of examples 34-36, wherein the set of acts further comprises determining a display order for the normal cell-type images in the subset of images of cells, wherein the display order is different from an order in which the normal cell-type images were received.

[0128] Example 38

[0129] The system of example 37, wherein determining the display order for the normal cell-type images in the subset of cell images comprises: determining a display order for the normal cell-type images in the images of cells from the biological sample by shuffling the normal cell-type images of cells from the biological sample out of an order in which they were received using pseudorandom shuffling, wherein the pseudorandom shuffling provides the display order for the normal cell-type images in the images of cels from the biological sample based on a seed for the biological sample; and extracting the display order for the normal celltypes images in the subset of cell images from the display order for the normal cell-type images in the images of cells from the biological sample.

[0130] Example 39

[0131] The system of example 38, wherein: the set of acts comprises redetermining the display order for the normal cell-type images in the images of cells from the biological sample using the pseudorandom shuffling and the seed for the biological sample; and the display order from determining the display order for the normal cell-type images in the images of cells from the biological sample is the same as the display order from redetermining the display order for the normal cell-type images in the images of cells from the biological sample.

[0132] Example 40

[0133] The system of any of examples 38-39, wherein the system is configured to generate the seed for the biological sample upon receipt of the biological sample.

[0134] Example 41

[0135] The system of any of examples 34-40, wherein the set of acts comprises determining the number of normal cell-type images to include in the subset of images of cells from the biological sample based on, for each abnormal cell-type image in the subset of imageof cells from the biological sample, reducing the number of images of normal cell-type images from an initial number.

[0136] Example 42

[0137] The system of any of examples 34-41, wherein: the normal cell-type images included in the images of cells from the biological sample comprise images of a plurality of cell-types; and the set of acts comprises determining how may images of normal cell-type images for each of the plurality of cell-types to include in the subset of the images of cells from the biological sample using a rounding-reallocation method.

[0138] Example 43

[0139] The system of any of examples 34-42, wherein: the system comprises a biological analyzer comprising a flowcell and a camera; and receiving images of cells from the biological sample comprises: establishing a flow of alignment fluid from an alignment fluid reservoir into the flowcell; creating a sample stream comprising the blood sample and a sheath of alignment fluid surrounding the sample fluid based on injecting the sample fluid from a channel into the flow of alignment fluid; and using the camera to capture a plurality of images as the sample stream is flowing through a viewing area of the flowcell.

[0140] Example 44

[0141] A method comprising performing the set of acts the instructions stored on the non- transitory computer readable mediums of the system of any of examples 24-43 are to perform when executed.

[0142] Example 45

[0143] A non-transitory computer readable medium storage medium comprising instructions to perform the set of acts which the instructions stored on the non-transitory computer readable mediums of the system of any of examples 24-43 are to perform when executed.

[0144] Example 46

[0145] A computer program product comprising instructions which, when the program is executed by a computer, cause the computer to carry out the set of acts which the instructions stored on the non-transitory computer readable mediums of the system of any of examples 24- 43 are to perform when executed.

[0146] Example 47

[0147] An analyzer comprising: one or more processors; a camera; and one or more non- transitory computer readable mediums storing instructions to, when executed, perform a set of acts comprising: receiving images of cells from a biological sample, the images including first cell-type images, and second cell-type images; establishing a first cell-type count, and a second cell-type count from the images, wherein the second cell-type is a subset of the first-cell-type; and assigning a number of display images to the second cell-type, wherein the number of display images assigned to the second cell-type is based on a relationship of the second celltype count to the first cell-type count.

[0148] Example 48

[0149] The analyzer of example 47, wherein the first cell-type is white blood cells and the second cell-type is selected from neutrophils, lymphocytes, monocytes, eosinophils, and basophils.

[0150] Example 49

[0151] The analyzer of any of examples 47-48, wherein the set of acts further comprises determining a display order for a set of the second cell-type images, wherein the display order is different from an order in which the set of the second cell-type images were received.

[0152] Example 50

[0153] The analyzer of example 49, wherein determining the display order for the set of the second cell-type images comprises: determining a display order for the first cell-type images by shuffling the first cell-type images out of the order in which they were received using pseudorandom shuffling, wherein the pseudorandom shuffling provides the display order for the first cell-type images based on a seed for the biological sample; and extracting the display order for the set of the second cell-type images from the display order for the first celltype images.

[0154] Example 51

[0155] The analyzer of example 50, wherein: the set of acts comprises redetermining the display order for the first cell-type images using the pseudorandom shuffling and the seed for the biological sample; and the display order from determining the display order for the first cell-type images is the same as the display order from redetermining the display order for first cell-type images.

[0156] Example 52

[0157] The analyzer of any of examples 50-51, wherein the seed for the biological sample is a seed the system is configured to generate for the sample upon receipt of the biological sample.

[0158] Example 53

[0159] The analyzer of any of examples 47-52, wherein the set of acts comprises, for each of a set of abnormal cell-types, determining whether to highlight that abnormal cell-type by displaying a number of images of cells from the biological sample having that abnormal celltype on an initial portion of a user interface.

[0160] Example 54

[0161] The analyzer of example 53, wherein: the set of acts comprises determining a number of display images for the first cell-type based on, for each abnormal cell-type from theset of abnormal cell-types determined to be highlighted, reducing the number of display images for the first cell-type from an initial number based on the number of images of cells having that abnormal cell-type displayed on the initial portion of the user interface; and assigning the number of display images to the second cell-type comprises assigning a portion of the number of display images for the first cell-type to the second cell-type based on a proportion of the second cell-type in the first cell-type.

[0162] Example 55

[0163] The analyzer of any of examples 47-54, wherein the system is configured to assign the number of display images to the second cell-type using a rounding-reallocation method.

[0164] Example 56

[0165] The analyzer of any of examples 47-55, wherein: the analyzer comprises a flowcell;receiving images of cells from the biological sample comprises: establishing a flow of alignment fluid from an alignment fluid reservoir into the flowcell; creating a sample stream comprising the blood sample and a sheath of alignment fluid surrounding the sample fluid based on injecting the sample fluid from a channel into the flow of alignment fluid; and using the camera to capture a plurality of images as the sample stream is flowing through a viewing area of the flowcell.

[0166] Example 57

[0167] An analyzer comprising: one or more processors; a camera; and one or more non- transitory computer readable mediums storing instructions to, when executed, perform a set of acts comprising: receiving images of cells from a biological sample, the images comprising abnormal cell-type images and normal cell-type images; identifying abnormal cell-type images from among the images of cells from the biological sample; and displaying a subset of the images of cells from the biological sample on a user display, wherein the subset includes both abnormal cell-type images and normal cell-type images.

[0168] Example 58

[0169] The analyzer of example 57, wherein: the abnormal cell-type images comprise images of one or more of blastocysts, atypical lymphocytes, giant platelets, platelet clumps, RBC inclusions, and malformed RBCs; and the normal cell-types comprise images of one or more of neutrophils, lymphocytes, monocytes, eosinophils, basophils, red blood cells and platelets.

[0170] Example 59

[0171] The analyzer of any of examples 57-58, wherein: the abnormal cell-type images comprise abnormal cell-type images depicting cells of a first abnormal cell-type; the subset of the images of cells from the biological sample comprises one or more images depicting cells of the first abnormal cell-type; displaying the subset of the images of cells from the biological sample comprises displaying the subset of images of cells from the biological sample on an initial portion of a user interface; and the set of acts comprises, prior to displaying the subset of the images of cells from the biological sample, determining whether to display one or more images depicting cells of the first abnormal cell-type based on whether a prevalence of cells of the first abnormal cell-type exceeds a threshold.

[0172] Example 60

[0173] The analyzer of any of examples 57-59, wherein the set of acts further comprises determining a display order for the normal cell- type images in the subset of images of cells, wherein the display order is different from an order in which the normal cell-type images were received.

[0174] Example 61

[0175] The analyzer of example 60, wherein determining the display order for the normal cell-type images in the subset of cell images comprises: determining a display order for the normal cell-type images in the images of cells from the biological sample by shuffling thenormal cell-type images of cells from the biological sample out of an order in which they were received using pseudorandom shuffling, wherein the pseudorandom shuffling provides the display order for the normal cell-type images in the images of cels from the biological sample based on a seed for the biological sample; and extracting the display order for the normal cell- types images in the subset of cell images from the display order for the normal cell-type images in the images of cells from the biological sample.

[0176] Example 62

[0177] The analyzer of example 61, wherein: the set of acts comprises redetermining the display order for the normal cell-type images in the images of cells from the biological sample using the pseudorandom shuffling and the seed for the biological sample; and the display order from determining the display order for the normal cell-type images in the images of cells from the biological sample is the same as the display order from redetermining the display order for the normal cell-type images in the images of cells from the biological sample.

[0178] Example 63

[0179] The analyzer of any of examples 61-62, wherein the analyzer is configured to generate the seed for the biological sample upon receipt of the biological sample.

[0180] Example 64

[0181] The analyzer of any of examples 57-63, wherein the set of acts comprises determining the number of normal cell-type images to include in the subset of images of cells from the biological sample based on, for each abnormal cell-type image in the subset of image of cells from the biological sample, reducing the number of images of normal cell-type images from an initial number.

[0182] Example 65

[0183] The analyzer of any of examples 57-64, wherein: the normal cell-type images included in the images of cells from the biological sample comprise images of a plurality ofcell-types; and the set of acts comprises determining how may images of normal cell-type images for each of the plurality of cell-types to include in the subset of the images of cells from the biological sample using a rounding-reallocation method.

[0184] Example 66

[0185] The analyzer of any of examples 57-65, wherein: the analyzer comprises a flowcell;and receiving images of cells from the biological sample comprises: establishing a flow of alignment fluid from an alignment fluid reservoir into the flowcell; creating a sample stream comprising the blood sample and a sheath of alignment fluid surrounding the sample fluid based on injecting the sample fluid from a channel into the flow of alignment fluid; and using the camera to capture a plurality of images as the sample stream is flowing through a viewing area of the flowcell.

[0186] Example 67

[0187] A method comprising performing the set of acts the instructions stored on the non- transitory computer readable mediums of the analyzer of any of examples 47-66 are to perform when executed.

[0188] Example 68

[0189] A non-transitory computer readable medium storage medium comprising instructions to perform the set of acts which the instructions stored on the non-transitory computer readable mediums of the analyzer of any of examples 47-66 are to perform when executed.

[0190] Example 69

[0191] A computer program product comprising instructions which, when the program is executed by a computer, cause the computer to carry out the set of acts which the instructions stored on the non-transitory computer readable mediums of the analyzer of any of examples 47-66 are to perform when executed.

[0192] Each of the calculations or operations described herein may be performed using a computer or other processor having hardware, software, and / or firmware. The various method steps may be performed by modules, and the modules may comprise any of a wide variety of digital and / or analog data processing hardware and / or software arranged to perform the method steps described herein. The modules optionally comprising data processing hardware adapted to perform one or more of these steps by having appropriate machine programming code associated therewith, the modules for two or more steps (or portions of two or more steps) being integrated into a single processor board or separated into different processor boards in any of a wide variety of integrated and / or distributed processing architectures. These methods and systems will often employ a tangible media embodying machine-readable code with instructions for performing the method steps described above. Suitable tangible media may comprise a memory (including a volatile memory and / or a non-volatile memory), a storage media (such as a magnetic recording on a floppy disk, a hard disk, a tape, or the like; on an optical memory such as a CD, a CD-R / W, a CD-ROM, a DVD, or the like; or any other digital or analog storage media), or the like.

[0193] All patents, patent publications, patent applications, journal articles, books, technical references, and the like discussed in the instant disclosure are incorporated herein by reference in their entirety for all purposes.

[0194] Different arrangements of the components depicted in the drawings or described above, as well as components and steps not shown or described are possible. For example, in some cases, aspects of processing described herein (e.g., training of machine learning algorithms, application of machine learning algorithms to images taken of patient samples) may be performed in various configurations - for instance, using a processor which his comprised by (or local to) an analyzer, a parallel-processing arrangement, or processing being performed remotely from the analyzer which captures images (such as using a cloud based platform, or using a remotely linked computer or system to process the analyzer data). Similarly, some features and sub-combinations are useful and may be employed without reference to other features and sub-combinations. Embodiments of the invention have beendescribed for illustrative and not restrictive purposes, and alternative embodiments will become apparent to readers of this patent. In certain cases, method steps or operations may be performed or executed in differing order, or operations may be added, deleted or modified. It can be appreciated that, in certain aspects of the invention, a single component may be replaced by multiple components, and multiple components may be replaced by a single component, to provide an element or structure or to perform a given function or functions. Except where such substitution would not be operative to practice certain embodiments of the invention, such substitution is considered within the scope of the invention. Accordingly, the claims should not be treated as limited to the examples, drawings, embodiments and illustrations provided above, but instead should be understood as having the scope provided when their terms are given their broadest reasonable interpretation as provided by a general purpose dictionary, except that when a term or phrase is indicated as having a particular meaning under the heading Explicit Definitions, it should be understood as having that meaning when used in the claims.

[0195] EXPLICIT DEFINITIONS

[0196] It should be understood that, in the above examples and the claims, a statement that something is “based on” something else should be understood to mean that it is determined at least in part by the thing that it is indicated as being based on. To indicate that something must be completely determined based on something else, it is described as being “based EXCLUSIVELY on” whatever it must be completely determined by.

[0197] It should be understood that, in the above examples and claims, the term “set” should be understood as one or more things which are grouped together.

Claims

What is claimed is:

1. A computer implemented method comprising:receiving images of cells from a biological sample, the images including first cell-type images, and second cell-type images;establishing a first cell-type count, and a second cell-type count from the images, wherein the second cell-type is a subset of the first-cell-type; andassigning a number of display images to the second cell-type, wherein the number of display images assigned to the second cell-type is based on a relationship of the second cell-type count to the first cell-type count.

2. The method of claim 1, wherein the first cell-type is white blood cells and the second celltype is selected from neutrophils, lymphocytes, monocytes, eosinophils, and basophils.

3. The method of any of claims 1 to 2, wherein the method further comprises determining a display order for a set of the second cell-type images, wherein the display order is different from an order in which the set of the second cell-type images were received.

4. The method of claim 3, wherein determining the display order for the set of the second cell-type images comprises:determining a display order for the first cell-type images by shuffling the first cell-type images out of the order in which they were received using pseudorandom shuffling, wherein the pseudorandom shuffling provides the display order for the first cell-type images based on a seed for the biological sample; andextracting the display order for the set of the second cell-type images from the display order for the first cell-type images.

5. The method of claim 4, wherein:the method comprises redetermining the display order for the first cell-type images using the pseudorandom shuffling and the seed for the biological sample; andthe display order from determining the display order for the first cell-type images is the same as the display order from redetermining the display order for first cell-type images.

6. The method of any of claims 4-5, wherein the seed for the biological sample is a seed generated for the sample upon receipt of the biological sample.

7. The method of any of claims 1-6, wherein the method comprises, for each of a set of abnormal cell-types, determining whether to highlight that abnormal cell-type by displaying a number of images of cells from the biological sample having that abnormal cell-type on an initial portion of a user interface.

8. The method of claim 7, wherein:the method comprises determining a number of display images for the first cell-type based on, for each abnormal cell-type from the set of abnormal cell-types determined to be highlighted, reducing the number of display images for the first cell-type from an initial number based on the number of images of cells having that abnormal cell-type displayed on the initial portion of the user interface; andassigning the number of display images to the second cell-type comprises assigning a portion of the number of display images for the first cell-type to the second cell-type based on a proportion of the second cell-type in the first cell-type.

9. The method of any of claims 1-8, wherein assigning the number of display images to the second cell-type is performed using a rounding-reallocation method.

10. The method of any of claims 1-9, wherein receiving images of cells from the biological sample comprises:establishing a flow of alignment fluid from an alignment fluid reservoir into a flowcell;creating a sample stream comprising the blood sample and a sheath of alignment fluid surrounding the sample fluid based on injecting the sample fluid from a channel into the flow of alignment fluid; andusing a camera focused on a viewing area of the flowcell to capture a plurality of images as the sample stream is flowing through the viewing area of the flowcell.

11. A computer implemented method comprising:receiving images of cells from a biological sample, the images comprising abnormal celltype images and normal cell-type images;identifying abnormal cell-type images from among the images of cells from the biological sample; anddisplaying a subset of the images of cells from the biological sample on a user display, wherein the subset includes both abnormal cell-type images and normal cell-type images.

12. The method of claim 11, wherein:the abnormal cell-type images comprise images of one or more of blastocysts, atypical lymphocytes, giant platelets, platelet clumps, RBC inclusions, and malformed RBCs; and the normal cell-types comprise images of one or more of neutrophils, lymphocytes, monocytes, eosinophils, basophils, red blood cells and platelets.

13. The method of any of claims 11-12, wherein:the abnormal cell-type images comprise abnormal cell-type images depicting cells of a first abnormal cell-type;the subset of the images of cells from the biological sample comprises one or more images depicting cells of the first abnormal cell-type;displaying the subset of the images of cells from the biological sample comprises displaying the subset of images of cells from the biological sample on an initial portion of a user interface; andthe method comprises, prior to displaying the subset of the images of cells from the biological sample, determining whether to display one or more images depicting cells of the firstabnormal cell-type based on whether a prevalence of cells of the first abnormal cell-type exceeds a threshold.

14. The method of any of claims 11-13, wherein the method further comprises determining a display order for the normal cell-type images in the subset of images of cells, wherein the display order is different from an order in which the normal cell-type images were received.

15. The method of claim 14, wherein determining the display order for the normal cell-type images in the subset of cell images comprises:determining a display order for the normal cell-type images in the images of cells from the biological sample by shuffling the normal cell-type images of cells from the biological sample out of an order in which they were received using pseudorandom shuffling, wherein the pseudorandom shuffling provides the display order for the normal cell-type images in the images of cels from the biological sample based on a seed for the biological sample; andextracting the display order for the normal cell-types images in the subset of cell images from the display order for the normal cell-type images in the images of cells from the biological sample.

16. The method of claim 15, wherein:the method comprises redetermining the display order for the normal cell-type images in the images of cells from the biological sample using the pseudorandom shuffling and the seed for the biological sample; andthe display order from determining the display order for the normal cell-type images in the images of cells from the biological sample is the same as the display order from redetermining the display order for the normal cell-type images in the images of cells from the biological sample.

17. The method of any of claims 15-16, wherein the seed for the biological sample is a seed generated for the biological sample upon receipt of the biological sample.

18. The method of any of claims 11-17, wherein the method comprises determining the number of normal cell-type images to include in the subset of images of cells from the biological sample based on, for each abnormal cell-type image in the subset of image of cells from the biological sample, reducing the number of images of normal cell-type images from an initial number.

19. The method of any of claims 11-18, wherein:the normal cell-type images included in the images of cells from the biological sample comprise images of a plurality of cell-types; andthe method comprises determining how may images of normal cell-type images for each of the plurality of cell-types to include in the subset of the images of cells from the biological sample using a rounding-reallocation method.

20. The method of any of claims 11-19, wherein receiving images of cells from the biological sample comprises:establishing a flow of alignment fluid from an alignment fluid reservoir into a flowcell; creating a sample stream comprising the blood sample and a sheath of alignment fluid surrounding the sample fluid based on injecting the sample fluid from a channel into the flow of alignment fluid; andusing a camera focused on a viewing area of the flowcell to capture a plurality of images as the sample stream is flowing through the viewing area of the flowcell.

21. A system, comprising:one or more processors; anda non-transitory computer readable medium having stored thereon instructions operable to, when executed by the one or more processors, perform the method of any preceding claim.

22. A non-transitory computer-readable storage medium comprising instructions which, when executed by a computer, cause the computer to carry out the steps of any one of the methods of claims 1 to 20.

23. A computer program product comprising instructions which, when the program is executed by a computer, cause the computer to carry out the steps of any one of the methods of claims 1 to 20.

24. A system comprising:one or more processors;one or more non-transitory computer readable mediums storing instructions to, when executed, perform a set of acts comprising:receiving images of cells from a biological sample, the images including first celltype images, and second cell-type images;establishing a first cell-type count, and a second cell-type count from the images, wherein the second cell-type is a subset of the first-cell-type; andassigning a number of display images to the second cell-type, wherein the number of display images assigned to the second cell-type is based on a relationship of the second cell-type count to the first cell-type count.

25. The system of claim 24, wherein the first cell-type is white blood cells and the second celltype is selected from neutrophils, lymphocytes, monocytes, eosinophils, and basophils.

26. The system of any of claims 24-25, wherein the set of acts further comprises determining a display order for a set of the second cell-type images, wherein the display order is different from an order in which the set of the second cell-type images were received.

27. The system of claim 26, wherein determining the display order for the set of the second cell-type images comprises:determining a display order for the first cell-type images by shuffling the first cell-type images out of the order in which they were received using pseudorandom shuffling, wherein the pseudorandom shuffling provides the display order for the first cell-type images based on a seed for the biological sample; andextracting the display order for the set of the second cell-type images from the display order for the first cell-type images.

28. The system of claim 27, wherein:the set of acts comprises redetermining the display order for the first cell-type images using the pseudorandom shuffling and the seed for the biological sample; andthe display order from determining the display order for the first cell-type images is the same as the display order from redetermining the display order for first cell-type images.

29. The system of any of claims 27-28, wherein the seed for the biological sample is a seed the system is configured to generate for the sample upon receipt of the biological sample.

30. The system of any of claims 24-29, wherein the set of acts comprises, for each of a set of abnormal cell-types, determining whether to highlight that abnormal cell-type by displaying a number of images of cells from the biological sample having that abnormal cell-type on an initial portion of a user interface.

31. The system of claim 30, wherein:the set of acts comprises determining a number of display images for the first cell-type based on, for each abnormal cell-type from the set of abnormal cell-types determined to be highlighted, reducing the number of display images for the first cell-type from an initial number based on the number of images of cells having that abnormal cell-type displayed on the initial portion of the user interface; andassigning the number of display images to the second cell-type comprises assigning a portion of the number of display images for the first cell-type to the second cell-type based on a proportion of the second cell-type in the first cell-type.

32. The system of any of claims 24-31, wherein the system is configured to assign the number of display images to the second cell-type using a rounding-reallocation method.

33. The system of any of claims 24-32, wherein:the system comprises a biological analyzer comprising a flowcell and a camera; and receiving images of cells from the biological sample comprises:establishing a flow of alignment fluid from an alignment fluid reservoir into the flowcell;creating a sample stream comprising the blood sample and a sheath of alignment fluid surrounding the sample fluid based on injecting the sample fluid from a channel into the flow of alignment fluid; andusing the camera to capture a plurality of images as the sample stream is flowing through a viewing area of the flowcell.

34. A system comprising:one or more processors;one or more non-transitory computer readable mediums storing instructions to, when executed, perform a set of acts comprising:receiving images of cells from a biological sample, the images comprising abnormal cell-type images and normal cell-type images;identifying abnormal cell-type images from among the images of cells from the biological sample; anddisplaying a subset of the images of cells from the biological sample on a user display, wherein the subset includes both abnormal cell-type images and normal cell-type images.

35. The system of claim 34, wherein:the abnormal cell-type images comprise images of one or more of blastocysts, atypical lymphocytes, giant platelets, platelet clumps, RBC inclusions, and malformed RBCs; and the normal cell-types comprise images of one or more of neutrophils, lymphocytes, monocytes, eosinophils, basophils, red blood cells and platelets.

36. The system of any of claims 34-35, wherein:the abnormal cell-type images comprise abnormal cell-type images depicting cells of a first abnormal cell-type;the subset of the images of cells from the biological sample comprises one or more images depicting cells of the first abnormal cell-type;displaying the subset of the images of cells from the biological sample comprises displaying the subset of images of cells from the biological sample on an initial portion of a user interface; andthe set of acts comprises, prior to displaying the subset of the images of cells from the biological sample, determining whether to display one or more images depicting cells of the first abnormal cell-type based on whether a prevalence of cells of the first abnormal cell-type exceeds a threshold.

37. The system of any of claims 34-36, wherein the set of acts further comprises determining a display order for the normal cell-type images in the subset of images of cells, wherein the display order is different from an order in which the normal cell-type images were received.

38. The system of claim 37, wherein determining the display order for the normal cell-type images in the subset of cell images comprises:determining a display order for the normal cell-type images in the images of cells from the biological sample by shuffling the normal cell-type images of cells from the biological sample out of an order in which they were received using pseudorandom shuffling, wherein the pseudorandom shuffling provides the display order for the normal cell-type images in the images of cels from the biological sample based on a seed for the biological sample; andextracting the display order for the normal cell-types images in the subset of cell images from the display order for the normal cell-type images in the images of cells from the biological sample.

39. The system of claim 38, wherein:the set of acts comprises redetermining the display order for the normal cell-type images in the images of cells from the biological sample using the pseudorandom shuffling and the seed for the biological sample; andthe display order from determining the display order for the normal cell-type images in the images of cells from the biological sample is the same as the display order from redetermining the display order for the normal cell-type images in the images of cells from the biological sample.

40. The system of any of claims 38-39, wherein the system is configured to generate the seed for the biological sample upon receipt of the biological sample.

41. The system of any of claims 34-40, wherein the set of acts comprises determining the number of normal cell-type images to include in the subset of images of cells from the biological sample based on, for each abnormal cell-type image in the subset of image of cells from the biological sample, reducing the number of images of normal cell-type images from an initial number.

42. The system of any of claims 34-41, wherein:the normal cell-type images included in the images of cells from the biological sample comprise images of a plurality of cell-types; andthe set of acts comprises determining how may images of normal cell-type images for each of the plurality of cell-types to include in the subset of the images of cells from the biological sample using a rounding-reallocation method.

43. The system of any of claims 34-42, wherein:the system comprises a biological analyzer comprising a flowcell and a camera; and receiving images of cells from the biological sample comprises:establishing a flow of alignment fluid from an alignment fluid reservoir into the flowcell;creating a sample stream comprising the blood sample and a sheath of alignment fluid surrounding the sample fluid based on injecting the sample fluid from a channel into the flow of alignment fluid; andusing the camera to capture a plurality of images as the sample stream is flowing through a viewing area of the flowcell.

44. A method comprising performing the set of acts the instructions stored on the non-transitory computer readable mediums of the system of any of claims 24-43 are to perform when executed.

45. A non-transitory computer readable medium storage medium comprising instructions to perform the set of acts which the instructions stored on the non-transitory computer readable mediums of the system of any of claims 24-43 are to perform when executed.

46. A computer program product comprising instructions which, when the program is executed by a computer, cause the computer to carry out the set of acts which the instructions stored on the non-transitory computer readable mediums of the system of any of claims 24-43 are to perform when executed.

47. An analyzer comprising:one or more processors;a camera: andone or more non-transitory computer readable mediums storing instructions to, when executed, perform a set of acts comprising:receiving images of cells from a biological sample, the images including first celltype images, and second cell-type images;establishing a first cell-type count, and a second cell-type count from the images, wherein the second cell-type is a subset of the first-cell-type; andassigning a number of display images to the second cell-type, wherein the number of display images assigned to the second cell-type is based on a relationship of the second cell-type count to the first cell-type count.

48. The analyzer of claim 47, wherein the first cell-type is white blood cells and the second cell-type is selected from neutrophils, lymphocytes, monocytes, eosinophils, and basophils.

49. The analyzer of any of claims 47-48, wherein the set of acts further comprises determining a display order for a set of the second cell-type images, wherein the display order is different from an order in which the set of the second cell-type images were received.

50. The analyzer of claim 49, wherein determining the display order for the set of the second cell-type images comprises:determining a display order for the first cell-type images by shuffling the first cell-type images out of the order in which they were received using pseudorandom shuffling, wherein the pseudorandom shuffling provides the display order for the first cell-type images based on a seed for the biological sample; andextracting the display order for the set of the second cell-type images from the display order for the first cell-type images.

51. The analyzer of claim 50, wherein:the set of acts comprises redetermining the display order for the first cell-type images using the pseudorandom shuffling and the seed for the biological sample; andthe display order from determining the display order for the first cell-type images is the same as the display order from redetermining the display order for first cell-type images.

52. The analyzer of any of claims 50-51, wherein the seed for the biological sample is a seed the system is configured to generate for the sample upon receipt of the biological sample.

53. The analyzer of any of claims 47-52, wherein the set of acts comprises, for each of a set of abnormal cell-types, determining whether to highlight that abnormal cell-type by displaying a number of images of cells from the biological sample having that abnormal cell-type on an initial portion of a user interface.

54. The analyzer of claim 53. wherein:the set of acts comprises determining a number of display images for the first cell-type based on, for each abnormal cell-type from the set of abnormal cell-types determined to be highlighted, reducing the number of display images for the first cell-type from an initial number based on the number of images of cells having that abnormal cell-type displayed on the initial portion of the user interface; andassigning the number of display images to the second cell-type comprises assigning a portion of the number of display images for the first cell-type to the second cell-type based on a proportion of the second cell-type in the first cell-type.

55. The analyzer of any of claims 47-54, wherein the system is configured to assign the number of display images to the second cell-type using a rounding-reallocation method.

56. The analyzer of any of claims 47-55, wherein:the analyzer comprises a flowcell;receiving images of cells from the biological sample comprises:establishing a flow of alignment fluid from an alignment fluid reservoir into the flowcell;creating a sample stream comprising the blood sample and a sheath of alignment fluid surrounding the sample fluid based on injecting the sample fluid from a channel into the flow of alignment fluid; andusing the camera to capture a plurality of images as the sample stream is flowing through a viewing area of the flowcell.

57. An analyzer comprising:one or more processors;a camera; andone or more non-transitory computer readable mediums storing instructions to, when executed, perform a set of acts comprising:receiving images of cells from a biological sample, the images comprising abnormal cell-type images and normal cell-type images;identifying abnormal cell-type images from among the images of cells from the biological sample; anddisplaying a subset of the images of cells from the biological sample on a user display, wherein the subset includes both abnormal cell-type images and normal cell-type images.

58. The analyzer of claim 57, wherein:the abnormal cell-type images comprise images of one or more of blastocysts, atypical lymphocytes, giant platelets, platelet clumps, RBC inclusions, and malformed RBCs; and the normal cell-types comprise images of one or more of neutrophils, lymphocytes, monocytes, eosinophils, basophils, red blood cells and platelets.

59. The analyzer of any of claims 57-58, wherein:the abnormal cell-type images comprise abnormal cell-type images depicting cells of a first abnormal cell-type;the subset of the images of cells from the biological sample comprises one or more images depicting cells of the first abnormal cell-type;displaying the subset of the images of cells from the biological sample comprises displaying the subset of images of cells from the biological sample on an initial portion of a user interface; andthe set of acts comprises, prior to displaying the subset of the images of cells from the biological sample, determining whether to display one or more images depicting cells of the first abnormal cell-type based on whether a prevalence of cells of the first abnormal cell-type exceeds a threshold.

60. The analyzer of any of claims 57-59, wherein the set of acts further comprises determining a display order for the normal cell-type images in the subset of images of cells, wherein the display order is different from an order in which the normal cell-type images were received.

61. The analyzer of claim 60, wherein determining the display order for the normal cell-type images in the subset of cell images comprises:determining a display order for the normal cell-type images in the images of cells from the biological sample by shuffling the normal cell-type images of cells from the biological sample out of an order in which they were received using pseudorandom shuffling, wherein the pseudorandom shuffling provides the display order for the normal cell-type images in the images of cels from the biological sample based on a seed for the biological sample; andextracting the display order for the normal cell-types images in the subset of cell images from the display order for the normal cell-type images in the images of cells from the biological sample.

62. The analyzer of claim 61, wherein:the set of acts comprises redetermining the display order for the normal cell-type images in the images of cells from the biological sample using the pseudorandom shuffling and the seed for the biological sample; andthe display order from determining the display order for the normal cell-type images in the images of cells from the biological sample is the same as the display order from redetermining the display order for the normal cell-type images in the images of cells from the biological sample.

63. The analyzer of any of claims 61-62, wherein the analyzer is configured to generate the seed for the biological sample upon receipt of the biological sample.

64. The analyzer of any of claims 57-63, wherein the set of acts comprises determining the number of normal cell-type images to include in the subset of images of cells from the biological sample based on, for each abnormal cell-type image in the subset of image of cells from thebiological sample, reducing the number of images of normal cell-type images from an initial number.

65. The analyzer of any of claims 57-64, wherein:the normal cell-type images included in the images of cells from the biological sample comprise images of a plurality of cell-types; andthe set of acts comprises determining how may images of normal cell-type images for each of the plurality of cell-types to include in the subset of the images of cells from the biological sample using a rounding-reallocation method.

66. The analyzer of any of claims 57-65. wherein:the analyzer comprises a flowcell; andreceiving images of cells from the biological sample comprises:establishing a flow of alignment fluid from an alignment fluid reservoir into the flowcell;creating a sample stream comprising the blood sample and a sheath of alignment fluid surrounding the sample fluid based on injecting the sample fluid from a channel into the flow of alignment fluid; andusing the camera to capture a plurality of images as the sample stream is flowing through a viewing area of the flowcell.

67. A method comprising performing the set of acts the instructions stored on the non-transitory computer readable mediums of the analyzer of any of claims 47-66 are to perform when executed.

68. A non-transitory computer readable medium storage medium comprising instructions to perform the set of acts which the instructions stored on the non-transitory computer readable mediums of the analyzer of any of claims 47-66 are to perform when executed.

69. A computer program product comprising instructions which, when the program is executed by a computer, cause the computer to carry out the set of acts which the instructions stored on the non-transitory computer readable mediums of the analyzer of any of claims 47-66 are to perform when executed.

Images