Biological sample analysis techniques

Abstract

Described herein are systems, devices, and methods allowing for a subset of images to be used to establish a population count for biological material (e.g., blood cells).

Description

TITLEBIOLOGICAL SAMPLE ANALYSIS TECHNIQUESCROSS REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of U.S. 63 / 526,247, filed on July 12, 2023, the entirety of which is incorporated by reference herein.BACKGROUND

[0002] Generally, this application relates to instruments / analyzers used for biological analysis incorporating imaging. In some examples, the instruments analyze biological particles, including biological cellular material, such as blood cells.

[0003] Imaging systems used for biological analysis can receive pictures of the material and determine quantity information (e.g., a cell count or particle count) from these images. However, in some applications, there can be a resource constraint in looking at large numbers of images and establishing a count. For instance, the computing processes involved can slow down throughput (e.g., the number of samples an instrument can run), or require high levels of system memory / processing, increasing cost and complexity and slowing down operations.

[0004] A newer class of technology utilizes flow imaging that employs a flowcell with a sample flowing therein. Images of different portions of the sample are obtained by optical imaging. The images may be obtained in succession as the sample flows through the flowcell. In this way, images of biological material are taken sequentially (e.g., on a cell-by-cell basis). However, as described above, there can be challenges with reviewing a large amount (e.g., thousands) of images and establishing a cell count for a population of interest based on reviewing such a large number of images.

[0005] There is therefore a need to have a biological imaging system which can establish information about a sample (e.g., particle count) without needing to review a large number of images.SUMMARY

[0006] According to embodiments, a computer-implemented method for analyzing a biological sample includes: receiving a plurality of images of the biological sample; determining a first-particle-type subset quantity information for a first particle type from a first subset of the plurality of images; obtaining a first-particle-type precision parameter; and utilizing the first-particle-type subset quantity information and the first-particle-type precision parameter to determine a first-particle-type population quantity information for the first particle type.

[0007] According to embodiments, a computer-implemented method for analyzing a biological sample includes: receiving a plurality of images of the biological sample; determining a first-particle-type subset quantity information for a first particle type from a first subset of the plurality of images; utilizing the first-particle-type subset quantity information to determine a second subset of the plurality of images to analyze, the second subset being larger than the first subset; and determining a first-particle-type population quantity information for the first particle type from the second subset of the plurality of images.

[0008] According to embodiments, a computer-implemented method for analyzing a biological sample includes: receiving a plurality of images of a biological sample; analyzing a first subset of the plurality of images to determine a first-particle-type subset quantity information for a first particle type; determining a first-particle-type population quantity information for the first particle type by applying a scaling factor to the first-population-type subset quantity information

[0009] According to embodiments, in any one of the computer-implemented method embodiments, the first particle type is one of an erythrocyte, a reticulocyte, a nucleated red blood cell, a platelet, or a white blood cell.

[0010] According to embodiments, in any one of the computer-implemented method embodiments, the first-particle-type precision parameter corresponds to a standard deviation of accuracy required for the first-particle-type population quantity information.[Oil] According to embodiments, in any one of the computer-implemented method embodiments, the first-particle-type precision parameter corresponds to a percentage of images in the first subset of the plurality of images that include instances of the first particle type.

[0012] According to embodiments, in any one of the computer-implemented method embodiments, determining the first-particle-type population quantity information further comprises utilizing the first-particle-type precision parameter and the first-particle-type subset quantity information to determine a second subset of the plurality of images to analyze, the second subset being larger in number than the first subset, and analyzing the second subset of the plurality of images.

[0013] According to embodiments, in any one of the computer-implemented method embodiments, the method further comprises determining a first-particle-type modified subset quantity information from analyzing the second subset of the plurality of images, and utilizing the first-particle-type modified subset quantity information to determine the first-particle-type population quantity information.

[0014] According to embodiments, in any one of the computer-implemented method embodiments, determining the first-particle-type population quantity information further comprises applying a scaling factor to the first-particle-type modified subset quantity information.

[0015] According to embodiments, in any one of the computer- implemented method embodiments, the first-particle-type precision parameter corresponds to a predetermined value.

[0016] According to embodiments, in any one of the computer-implemented method embodiments, the method further comprises determining a second-particle-type subset quantity information for a second particle type from the first subset, obtaining a second- particle-type precision parameter, and determining a second-particle-type population quantity information for the second particle type by utilizing the second-particle-type precision parameter and the second-particle-type subset quantity information.

[0017] According to embodiments, in any one of the computer-implemented method embodiments, the first particle type is one of an erythrocyte, a reticulocyte, a nucleated red blood cell, a platelet, or a white blood cell, and wherein the second particle type is a differentone of an erythrocyte, a reticulocyte, a nucleated red blood cell, a platelet, or a white blood cell.

[0018] According to embodiments, in any one of the computer-implemented method embodiments, the first-particle-type precision parameter and the second-particle-type precision parameter comprise different numbers.

[0019] According to embodiments, in any one of the computer-implemented method embodiments, the first-particle-type subset quantity information is one of a count of the first particle type obtained from the first subset of images, or a number of images of the first subset of images containing the first particle type.

[0020] According to embodiments, in any one of the computer-implemented method embodiments, the first-particle-type population quantity information is one of a cell count or a cell concentration.

[0021] According to embodiments, a biological sample analyzer includes: a camera configured to obtain a plurality of images of particles of a biological sample; a processor; and a computer- readable medium storing instructions that, when executed by the processor, cause the processor to: receive the plurality of images; determine a first-particle-type subset quantity information for a first particle type from a first subset of the plurality of images; obtain a first-particle-type precision parameter; and utilize the first-particle-type subset quantity information and the first- particle-type precision parameter to determine a first-particle-type population quantity information for the first particle type.

[0022] According to embodiments, a biological sample analyzer includes: a camera configured to take a plurality of images of particles of a biological sample; a processor; and a computer- readable medium storing instructions that, when executed by a processor, causes the processor to: receive the plurality of images; determine a first-particle-type subset quantity information for a first particle type from a first subset of the plurality of images; utilize the first-particle- type quantity information to determine a second subset of the plurality of images to analyze, the second subset being larger than the first subset; and determine a first-particle-type population quantity infomiation for the first particle type from the second subset of the plurality of images.

[0023] According to embodiments, a biological sample analyzer includes: a camera configured to take a plurality of images of particles of the biological sample; a processor; and a computer- readable medium storing instructions that, when executed by a processor, cause the processor to: receive a plurality of images; determine a first-particle-type subset quantity information for a first particle type from a first subset of the plurality of images; and determine a first-particletype population quantity information for the first particle type by applying a scaling factor to the first-particle-type quantity infomiation.

[0024] According to embodiments, in any one of the biological sample analyzer embodiments, the first particle type is one of an erythrocyte, a reticulocyte, a nucleated red blood cell, a platelet, or a white blood cell.

[0025] According to embodiments, in any one of the biological sample analyzer embodiments, the first-particle-type precision parameter corresponds to a standard deviation of accuracy required for the first-particle-type population quantity information.

[0026] According to embodiments, in any one of the biological sample analyzer embodiments, the first-particle-type precision parameter corresponds to a percentage of images in the first subset of the plurality of images that include instances of the first particle type.

[0027] According to embodiments, in any one of the biological sample analyzer embodiments, the computer-readable medium storing instructions that, when executed by the processor further cause the processor to determine the first-particle-type population quantity information by utilizing the first-particle-type precision parameter and the first-particle-type subset quantity information to determine a second subset of the plurality of images to analyze, the second subset being larger in number than the first subset, and analyzing the second subset of the plurality of images.

[0028] According to embodiments, in any one of the biological sample analyzer embodiments, the computer-readable medium storing instructions that, when executed by the processor further cause the processor to determine a first-particle-type modified subset quantity information from analyzing the second subset of the plurality of images, and utilize the first- particle-type modified subset quantity information to determine the first-particle-type population quantity information.

[0029] According to embodiments, in any one of the biological sample analyzer embodiments, the computer-readable medium storing instructions that, when executed by the processor further cause the processor to determine the first-particle-type population quantity information by applying a scaling factor to the first-particle-type modified subset quantity infomiation.

[0030] According to embodiments, in any one of the biological sample analyzer embodiments, the first-particle-type precision parameter corresponds to a predetermined value.

[0031] According to embodiments, in any one of the biological sample analyzer embodiments, the computer-readable medium storing instructions that, when executed by the processor further cause the processor to determine a second-particle-type subset quantity information for a second particle type from the first subset, obtain a second-particle-type precision parameter, and determine a second-particle-type population quantity information for the second particle type by utilizing the second-particle-type precision parameter and the second-particle-type subset quantity information.

[0032] According to embodiments, in any one of the biological sample analyzer embodiments, the first particle type is one of an erythrocyte, a reticulocyte, a nucleated red blood cell, a platelet, or a white blood cell, and wherein the second particle type is a different one of an erythrocyte, a reticulocyte, a nucleated red blood cell, a platelet, or a white blood cell.

[0033] According to embodiments, in any one of the biological sample analyzer embodiments, the first-particle -type precision parameter and the second-particle-type precision parameter comprise different numbers.

[0034] According to embodiments, in any one of the biological sample analyzer embodiments, the first-particle-type subset quantity information is one of a count of the first particle type obtained from the first subset of images, or a number of images of the first subset of images containing the first particle type.

[0035] According to embodiments, in any one of the biological sample analyzer embodiments, the first-particle-type population quantity infomiation is one of a cell count or a cell concentration.

[0036] According to embodiments, in any one of the biological sample analyzer embodiments, the first particle type is one of an erythrocyte, a reticulocyte, a nucleated red blood cell, a platelet, or a white blood cell.

[0037] According to embodiments, in any one of the biological sample analyzer embodiments, the computer-readable medium storing instruction that, when executed by the processor, further cause the processor to determine a first-particle-type modified subset quantity information from analyzing the second subset of the plurality of images, and utilize the first- particle-type modified subset quantity information to determine the first-particle-type population quantity information.

[0038] According to embodiments, in any one of the biological sample analyzer embodiments, the computer-readable medium storing instruction that, when executed by the processor, further cause the processor to determine the first-particle-type population quantity information by applying a scaling factor to the first-particle-type modified subset quantity information.

[0039] According to embodiments, in any one of the biological sample analyzer embodiments, the first-particle-type subset quantity information is one of a count of the first particle type obtained from the first subset of images, or a number of images of the first subset of images containing the first particle type.

[0040] According to embodiments, in any one of the biological sample analyzer embodiments, the first-particle-type population quantity infomiation is one of a cell count or a cell concentration.BRIEF DESCRIPTION OF SEVERAL VIEWS OF THE DRAWINGS

[0041] FIG. 1 is a schematic illustration, partly in section and not to scale, showing operational aspects of an exemplary flowcell, which may be used in an analyzer configured to capture and analyze images.

[0042] FIG. 2 is a flowchart for a method of determining population quantity information for one or more particles in a sample, according to embodiments.

[0043] FIG. 3 is a flowchart for a method of determining population quantity information for one or more particles in a sample, according to embodiments.

[0044] FIG. 4 is a flowchart for a method of determining population quantity information for one or more particles in a sample, according to embodiments.

[0045] FIG. 5 is a flowchart for a method of determining population quantity information for one or more particles in a sample, according to embodiments.

[0046] The foregoing summary, as well as the following detailed description of certain techniques of the present application, will be better understood when read in conjunction with the appended drawings. For the purposes of illustration, certain techniques are shown in the drawings. It should be understood, however, that the claims are not limited to the arrangements and instrumentality shown in the attached drawings.DETAILED DESCRIPTION

[0047] FIG. 1 schematically illustrates a biological sample analyzer, and particularly a system utilizing flow imaging principles. Though this disclosure provides embodiments of a biological analysis system utilizing flow imaging, the techniques may be applicable to other types of biological sample analyzers, as will be understood (e.g., static or slide imaging). The system of FIG. 1 includes an exemplary flowcell 22, which may be used in the analyzer for conveying a sample fluid through a viewing zone 23 of an optical imaging device 24 (e.g., a camera) in a configuration for imaging microscopic particles in a sample flow stream 32 using digital image processing. Flowcell 22 may be coupled to a source 25 of sample fluid, which may have been previously processed, such as through contact with a particle contrast agent composition and heating / incubation. Flowcell 22 may also be coupled to one or more sources 27 of a sheath fluid, such as a clear glycerol solution having a viscosity that is greater than the viscosity of the sample fluid, an example of which is disclosed in U.S. Pat. Nos. 9,316,635 and 10,451,612, the disclosures of which are herein incorporated by reference in their entireties. For the purposes of terminology, a sheath fluid is traditionally used to envelope a sample stream for analysis not involved imaging, for instance in circumstances where a sample is surrounded by sheath and subject to laser or light excitation to determine a fluorescence or light scatter property. In the context of this application, particle and / or intracellular organelle alignment liquid (PIAOL) is a type of sheath fluid customized for imaging applications, that is in the context of FIG. 1 where a sample (e.g., blood sample) is surrounded by a sheath fluidthrough an imaging region of a flowcell. The terms sheath, sheath fluid, and PIAOL may be used interchangeably for the purposes of this application.

[0048] The sample fluid may be injected through an opening (e.g., flattened opening) at a distal end 28 of a sample feed tube 29, and into at a point in the interior of the flowcell 22 where the sheath fluid flow may be substantially established, resulting in a stable and symmetric laminar flow of the sheath fluid above and below (or on opposing sides of) a sample stream (e.g., ribbon-shaped). The sample stream and sheath fluid stream may be forced by metering pumps (not shown) that move the sheath fluid with the sample fluid along a flowpath that narrows. The sheath fluid envelopes and compresses the sample fluid in zone 21 (e.g., a narrowing zone) where the flowpath narrows. The decrease in flowpath thickness in zone 21 can contribute to a geometric focusing of a sample flow stream 32. The sample flow stream 32 may be enveloped by the sheath fluid and conveyed downstream from zone 21, passing in front of, or otherwise through viewing zone 23 of, an optical imaging device 24 where images are obtained, for example, using an image sensor 48, such as a CCD. Processor 18 may receive data from image sensor 48. Processor 18 may include one or more processors. Processor 18 may control the operation of flowcell 22 and / or optical imaging device 24. Data from image sensor 48 (e.g., images) may be stored in a memory (e.g., non-volatile memory). Processor 18 may process data from image sensor 48, for example, by retrieving image data from memory. Processor 18 may execute instructions stored on a memory (which may or may not be the same memory in which image data is stored). The executed instructions may cause processor 18 to function as described herein. The sample flow stream 32 flows together with the sheath fluid to a discharge 33.

[0049] As shown here, 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 forced through the distal end 28 of sample tube 29 at a location that is distal to the proximal portion 21a. Hence, the sample fluid can enter the sheath fluid envelope as the sheath fluid stream is compressed by the zone 21 , wherein the sample tube 29 has an exit port through which sample fluid is injected into flowing sheath fluid, the exit port bounded by a narrowing region of zone 21.

[0050] The optical imaging device 24 with objective lens 46 is directed along an optical axis that intersects the sample flow stream 32. The relative distance between the objective lens 46 and the sample flow stream 32 is variable by operation of a motor drive 54 coupled (e.g., indirectly coupled as depicted) to the objective lens 46. The objective lens 46 can be repositioned to focus an image onto image sensor 48.

[0051] FIG. 1 further illustrates a light source 42, which may illuminate sample stream 32 during imaging. According to some embodiments, an autofocus pattern 44 can have a position that is fixed relative to the flowcell 22, and that is located at a displacement distance from the plane of the sample stream 32. In the embodiment shown, the autofocus pattern 44 is applied directly to the flowcell 22 at a location that is visible in an image collected through viewport 57 by optical imaging device 24. The autofocus pattern 44 can be used to set a correct focal location of the objective lens relative to the flowcell, for instance by setting a location where the autofocus pattern 44 is in its ideal focal state. In additional or alternative configurations, imaging processing of the biological material being imaged (e.g., blood cells) can be used to determine a focal position, for instance utilizing pixel analysis of the blood cells. Information on autofocusing or focal quality assessment can be found in U.S. Patent. Nos. 9,857,361, 10,794,900, 10,705,008, 11,543,340, 10,705,011, the disclosures of which are hereby incorporated by reference in their entireties.

[0052] Additional embodiments regarding the construction and operation of flowcells (e.g., flowcell 21) and optical imaging devices (e.g., optical imaging device 24) are disclosed in U.S. Pat. No. 9,322,752, entitled “Flowcell Systems and Methods for Particle Analysis in Blood Samples,” filed on March 17, 2014, which is herein incorporated by reference in its entirety. Embodiments disclosed below may be described in the context of the analysis system shown in FIG. 1 , but are not limited to such a system.

[0053] A biological sample analyzer, such as the one shown in FIG. 1 and utilizing flow cell 22 and optical imaging device 24, may acquire and analyze a number of images of a sample. The sample may include various types of particles, such as an erythrocyte, a reticulocyte, a nucleated red blood cell, a platelet, or a white blood cell. The biological sample analyzer can determine quantity information for one or more types of particles in the sample. For example, the biological sample analyzer can determine quantity information for a first particle type andquantity information for a second particle type (or quantity information for additional particle types). Quantity information for a given type of particle may include an actual count, an estimated count, a concentration of the given type of particle in the sample, a number of images that include the given type of particle, or other types of assessment of the quantity of the given type of particle in the sample. This disclosure may refer to one type of quantity information (e.g., count), but it is understood that other types of quantity information (e.g., number of images that include a given particle type) may be used as well. For counts of each particle type, there is a corresponding concentration of the particle type in the sample. A given concentration can be determined by dividing the number of particles in the sample (e g., a particle or cell count) by the volume of the sample. In order to determine the number of a given type of particle is present in a sample, the images of the sample may be analyzed by a processor (e.g., processor 18) to detect the given type of particle. Various computational approaches can be used to determine a particle type, for instance artificial intelligence or machine-learning based algorithms, pixel-based or feature-based analysis, image masking techniques, or other computational techniques. Information about such approaches can be found in U.S. Pat. Nos. 11,403,751, 6,947,586, 7,236,623, the disclosures of which are hereby incorporated by reference in their entireties.

[0054] Image analysis may be relatively computationally-heavy, and may therefore be relatively time consuming or may consume a relatively high amount of computing resources. For example, some types of artificial intelligence (“Al”) algorithms or models may be effective at identifying particles, but may be relatively computationally intense. Similarly, some types of image / pixel analysis based algorithms or models may also be effective at identifying particles, but also be computationally intense. If all of the images of the sample are analyzed, the process for determining concentration of a given particle type may be undesirably slow or consume an undesirably high amount of computing resources. In accordance with embodiments herein, it may be possible to analyze fewer than all of the images to estimate the count and / or concentration of a given particle type in a sample with sufficient accuracy. This estimate may be referred to as a “count,” even though every one of a given particle type in a sample has not been explicitly identified in an image. Furthermore, if the entire sample does not need to be imaged, it may be possible to obtain images of only a portion of the sample,which may also allow for consumption of fewer computing resources (e.g., less memory may be needed) and may allow for more rapid determination of counts and / or concentration.

[0055] FIG. 2 is a flowchart 200 for a method of determining one or more cell counts in a sample, according to embodiments. The steps in flowchart 200 may be performed by biological sample analyzer, such as the one shown in FIG. 1. The steps in flowchart 200 may be implemented by a computer. For example, the steps in flowchart 200 may be performed by a processor, such as processor 18. The steps may be performed when the processor executes instructions stored on a computer-readable memory (e.g., a non-transitory memory). The steps may be performed in sequence as shown, in a different sequence, and / or some of the steps may be performed in parallel or may overlap. For example, steps 220 and 260 may be performed at the same time, or overlappingly. As another example, steps 240 and 270 may be performed at the second time, or overlappingly. Some steps need not be performed, such as steps 250, 260, and / or 270. Flowchart 200 is described in conjunction with FIG. 1, but is not so limited.

[0056] At step 210, processor 18 receives image data of a biological sample, which includes at least one type of particle — for example, a first particle type and a second particle type. The image data may be obtained by optical imaging unit 24 in a manner described in the context of FIG. 1. The images may be of the entire biological sample, or only a portion thereof. If the images are for only a portion of the biological sample, then the images may be obtained from only a limited portion of the stream (e.g., a beginning portion, a middle portion, or an end portion), or may be obtained periodically but across the entire stream (e.g., 20% of the stream, but obtained at regular intervals). The images may be received by the processor 18 all at once, or over time — e.g., as coordinated by processor 18.

[0057] At step 220, processor 18 analyzes a first subset of the images received at step 210 to determine first-parti cle-type subset quantity information. Processor 18 analyzes the images to detect a first particle type. Processor 18 may employ an Al algorithm or model to detect instances of the first particle type in the first subset of images. Processor 18 may employ other types of algorithms to detect instances of the first particle type, such as edge detection or pixelanalysis algorithms. Each image in the first subset of the images may include zero, one, or more than one instance of the first particle type. Processor 18 adds all of the instances of the first particle type to determine a first-particle-type subset quantity information (e.g., a count ofinstances of the first particle type in the first subset of images). According to one embodiment, there is typically only one particle in a given image (if that image has a particle). Therefore, the first-particle-type subset quantity information may exactly or approximately correspond to the number of images with the first particle type.

[0058] At step 230, processor 18 obtains a first-particle-type precision parameter. The first- particle-type precision parameter may correspond to a predetermined preference as to how precise the quantity information for the first particle type needs to be. Some measures of quantity may not need a high level of precision, while others may benefit from greater precision. For example, the precision requirement for white blood cell counts may be less than the requirement for red blood cell counts. According to an embodiment, the first-particle-type precision parameter may be equal to or based on:1VA where N is or is based on the number of images that include the first particle type. N may, therefore, not be inclusive of images that do not include the first particle type. For example, if 50% of the total number of images of a sample include the first particle type, then A may be equal to or based on 50% of the total number of images. According to another embodiment, the first-particle-type precision parameter may be equal to or based on a standard deviation — e.g., a desired standard deviation of a count or concentration of the first particle type (e.g., the first-particle-type population quantity information discussed in step 240) in the sample. The precision parameter may be a predetermined value. There may be different desired precision parameters for different particle types — e.g., different predetermined values for the different particle types, such as a first-particle-type precision parameter and a second-particle-type precision parameter.

[0059] At step 240, processor 18 determines a first-particle-type population quantity information for the first particle type in the sample. The first-particle-type population quantity information for the first particle type may be an estimated quantity of the first particle type in the sample. The first-particle-type population quantity information may be determined by utilizing the first-particle-type subset quantity information and the first-particle-type precision parameter. For example, the first-particle-type population quantity information may bedetermined by analyzing a second subset of the images. The second subset of the images may or may not include the first subset of the images. The number of images in the second subset of images may be based on the first-particle-type subset quantity information and the first- particle-type precision parameter. The number of images in the second subset may be larger than the number of images in the first subset. According to an embodiment, the number of images in the second subset of images is or is based on:106a * c2where a is or corresponds to the number of images that include the first particle type (i.e., a corresponds to the first particle count), and c is or corresponds to the first-parti cl e-type precision parameter. The variables a and c may be expressed as a percentage * 100 (i.e., 15% = 15, not 0.15). Processor 18 may determine the number of instances of the first particle type in the second subset of images, instead of all of the images, to determine the first-particle-type population quantity information. As an alternative, the biological sample analyzer may only acquire a limited number of images of the sample — e.g., the number of images in the second subset of images — and the processor will analyze the limited number of images, or a subset of the limited number of images. Given that processor 18 is processing images of less than the entire sample, a scaling factor may be applied to the first-particle-type population quantity information. The scaling factor may be proportional to or correspond to the percentage of the sample that was actually analyzed by processor 18 (e.g., the percentage of the images that were analyzed in a subset from the entire set of images of the sample). Once scaled, an estimated quantity of the first particle type in the sample may be determined.

[0060] At step 250, processor 18 determines a first-parti cle-type modified subset quantity information for the first particle type. According to an embodiment, the number of images in the second subset of images described in step 240 includes buffer images. The buffer images may be a third subset of images (e.g., a relatively small number of images), and may be used to compensate for the first subset of images, for example, if the first subset of images is a relatively small amount of images. The quantity information for the first particle type in the buffer images may be used to determine the first-particle-type modified subset quantity information. The buffer images may be selected from the plurality of images in one or moreof the following ways: randomly, in between images in the first subset of images or the second subset of images, in earlier images than the first subset of images or the second subset of images, and / or in later images than the first subset of images or the second subset of images. In some examples, the buffer images can be a standard number (e.g., a certain number of images), or a percentage (e.g., a certain percentage associated with a cell population, or a certain percentage associated with a number of images analyzed).

[0061] According to another embodiment, processor 18 may determine a number of images that include an instance of the first particle type in step 250. In this regard, step 250 may be similar to step 220. This number may be used to update the first-particle-type precision parameter, as discussed in context of step 230. Step 240 may be performed again as well. In this way the first-particle-type modified subset quantity information may result from subsequent iterations of steps 230 and 240. It may be possible to perform one or more iterations.

[0062] Steps 220, 230, 240, and / or 250 may be performed for additional particle types (e.g., a second particle type, a third particle type, etc.). The discussion below provides additional context about second and additional particle types.

[0063] At step 260, processor 18 determines a a second-particle-type subset quantity information for a second particle type from the first subset of the images. During step 220 (or at a separate time), processor 18 may identify instances of a second particle type in the first subset of images, which is different from the first particle type. The process may be similar to that discussed with respect to the first particle type. It may be possible to identify subset quantity information for additional particle type(s) (e.g., third, fourth, etc.) in a similar manner.

[0064] At step 270, a second-particle-type precision parameter is obtained by processor 18. The second-particle-type precision parameter may be different from the first-particle-type precision parameter. For example, if less precision is required for determining a quantity of the second particle type, then the precision parameter may be lower. Step 270 may be similar to step 230, and the second-particle-type precision parameter may be obtained or determined in a similar manner as the first-particle-type precision parameter. Step 270 may be performed with step 230.

[0065] At step 280, processor 18 determines a second-particle-type population quantity information for a second particle type in the sample using the second-particle-type subset quantity information and the second-particle-type precision parameter. Step 280 may be performed with step 240 (or at a separate time). The process of determining the second- particle-type population quantity information for the second particle type may be similar to the process for determining the first-particle-type population quantity information of the first particle type. It may be possible to determine population quantity information for additional particle type(s) (e.g., third, fourth, etc.) in the sample in a similar manner. When processor 18 is analyzing images to identify instances of both the first particle type and the second particle type (or additional particle types) at the same time (or substantially the same time), the second subset of images may be determined, for example, by determining the maximum of the minimum number of images for each particle type.

[0066] FIG. 3 is a flowchart 300 for a method of determining population quantity information in a sample for a first particle type, according to embodiments. The steps in flowchart 300 may be performed by a biological sample analyzer, such as the one shown in FIG. 1, but this disclosure is not limited in this manner. The steps in flowchart 300 may be implemented by a computer. For example, the steps in flowchart 300 may be performed by a processor, such as processor 18. The steps may be performed when the processor executes instructions stored on a computer-readable memory (e.g., a non-transitory memory). The steps may be performed in sequence as shown, in a different sequence, and / or some of the steps may be performed in parallel or may overlap. The steps may be similar to those in flowchart 200 in FIG. 2. Step 310 may be similar to step 210. Step 320 may be similar to step 220. Step 330 may be similar to step 230. Step 340 may be similar to step 240. Flowchart 300 may be performed for a second particle type, a third particle type, etc. as well as the first particle type.

[0067] FIG. 4 is a flowchart 400 for a method of determining one or more population quantity information for particle type(s) in a sample, according to embodiments. The steps in flowchart 400 may be performed by a biological sample analyzer, such as the one shown in FIG. 1, but this disclosure is not limited in this manner. The steps in flowchart 400 may be implemented by a computer. For example, the steps in flowchart 400 may be performed by a processor, such as processor 18. The steps may be performed when the processor executes instructionsstored on a computer-readable memory (e g., a non-transitory memory). The steps may be performed in sequence as shown, in a different sequence, and / or some of the steps may be performed in parallel or may overlap.

[0068] At step 410, images of a sample are received. Step 410 may be similar to steps 210, 310. At step 420, first-particle-type subset quantity information for a first particle type in the sample is determined from a first subset of the images. Step 420 may be similar to steps 220, 320. At step 430, the number for a second subset of the images is determined using the first- particle-type subset quantity information. The number of images for the second subset may be determined in a manner similar to that discussed above in context of step 240 in flowchart 200. At step 440, first-particle-type population quantity information may be determined from the second subset of the images. The first-particle-type population quantity information may be determined in a manner similar to that discussed above with respect to step 240 in flowchart 200. Flowchart 400 may be performed for a second particle type, a third particle type, etc. as well as the first particle type.

[0069] FIG. 5 is a flowchart 500 for a method of determining one or more population quantity information for particle type(s) in a sample, according to embodiments. The steps in flowchart 500 may be performed by a biological sample analyzer, such as the one shown in FIG. 1, but this disclosure is not limited in this manner. The steps in flowchart 500 may be implemented by a computer. For example, the steps in flowchart 500 may be performed by a processor, such as processor 18. The steps may be performed when the processor executes instructions stored on a computer-readable memory (e.g., a non-transitory memory). The steps may be performed in sequence as shown, in a different sequence, and / or some of the steps may be performed in parallel or may overlap.

[0070] At step 510, images of a sample are received. Step 510 may be similar to steps 210, 310, 410. At step 520, first-particle-type subset quantity information for a first particle type in the sample is determined from a first subset of the images. Step 520 may be similar to steps 220, 320, 420. At step 530, a first-particle-type population quantity information in the sample is determined by applying a scaling factor to the first-particle-type subset quantity information determined at step 520. The scaling factor may be determined in a manner similar to thatdiscussed above with respect to step 240 in flowchart 200. Flowchart 500 may be performed for a second particle type, a third particle type, etc. as well as the first particle type.

[0071] The following is an example of an embodiment of the method described in context of the aforementioned flowcharts. A sample of blood, including platelets (the first particle type) is processed by a biological sample analyzer. The sample is 20 nanoliters. At step 210, the entire sample is imaged with 10,000 images and stored in memory. Processor 18 retrieves these images one at a time or otherwise in accordance with the discussion below. A first subset of these images are identified, which is for this example 100 images. At step 220, the first subset of images is analyzed to identify any platelets, and only 10 images have platelets, and there is only one platelet in each of these images. Therefore, 10% of the first subset of images have platelets. This is the first-particle-type count. Then, at step 230, the biological sample analyzer has a predetermined value that indicates that the first-particle-type precision parameter is 7%. In this example, the first-parti cl e-type precision parameter corresponds to the desired standard deviation for the final population count. The number of images in a second subset of images is determined by 106 / (10 * 72), which is approximately 2,041 images. Since 100 images have already been analyzed, this number is reduced to 1,941. Then, 200 images are added as buffer images, bringing the total number of images to 2,141. These 2,141 images are then analyzed to identify and count instances of platelets, or 21.41% of the total number of images. Processor 18 employs an artificial intelligence algorithm / model to identify instances of the platelets. Processor 18 counts 1,490 platelets. The 10 platelets identified in the 100 images of the first subset of images are added, so the total is 1,500 platelets in a total of 2,241 images (the first subset of images (100) is added to the 2,141 images). To estimate the total count of platelets, then, 1,500 is scaled up by dividing by 0.2241 (a scaling factor corresponding to the percentage of total images that were actually analyzed), which results in a count of 7,006 platelets. This number is an estimate. The concentration of platelets is then estimated to be 7,006 platelets per 20 nL (which is the volume of the sample in this example). This concentration can also be expressed as 350,300 platelets / pL. This number corresponds to the first-particle-type population quantity information discussed in context of step 240. Further iterations of step 240 may be performed pursuant to step 250, as will be understood. Moreover, the process can be used to count an additional particle, such as a reticulocyte, insteps 260 and 270. As an example for a reticulocyte, the precision parameter is 15% (less precision, because the standard deviation is greater). This corresponds to about 444 images in the second subset of images, which is less than the 2,041 determined for the platelet. The greater number of images (2,041) will be analyzed by processor 18 to account for the required precision for a platelet count.

[0072] Please note, the examples provided herein are meant to be illustrative. For instance, platelets are illustratively referred to as a first cell type and reticulocytes as a second cell type for the examples above, however, various types of blood cell types are contemplated. For instance, the first and second cell types can be chosen from among the group of at least: red blood cells, reticulocytes, platelet, neutrophils, lymphocytes, monocytes, eosinophils, and basophils.

[0073] 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.

[0074] 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.

[0075] Different arrangements of the components depicted in the drawings or described above, as well as components and steps not shown or described are possible. Similarly, some features and sub-combinations are useful and may be employed without reference to other features and sub-combinations. Embodiments of the invention have been described for illustrative and not restrictive purposes, and alternative embodiments will become apparent to readers of this patent. In certain cases, method steps or operations may be performed or executed in differing order, or operations may be added, deleted or modified. It can be appreciated that, in certain aspects of the invention, a single component may be replaced by multiple components, and multiple components may be replaced by a single component, to provide an element or structure or to perform a given function or functions.

[0076] It will be understood by those skilled in the art that various changes may be made and equivalents may be substituted without departing from the scope of the novel techniques disclosed in this application. In addition, many modifications may be made to adapt a particular situation or material to the teachings of the novel techniques without departing from its scope. Therefore, it is intended that the novel techniques not be limited to the particular techniques disclosed, but that they will include all techniques falling within the scope of the appended claims.

Claims

CLAIMS1. A computer-implemented method for analyzing a biological sample, comprising: receiving a plurality of images of the biological sample; determining a first-particle-type subset quantity information for a first particle type from a first subset of the plurality of images; obtaining a first-particle-type precision parameter; and utilizing the first-particle-type subset quantity information and the first-particle-type precision parameter to determine a first-parti cl e-type population quantity information for the first particle type.

2. The method of claim 1, wherein the first particle type is one of an erythrocyte, a reticulocyte, a nucleated red blood cell, a platelet, or a white blood cell.

3. The method of claim 1, wherein the first-particle-type precision parameter corresponds to a standard deviation of accuracy required for the first-particle-type population quantity information.

4. The method of claim 1, wherein the first-particle-type precision parameter corresponds to a percentage of images in the first subset of the plurality of images that include instances of the first particle type.

5. The method of claim 1, wherein determining the first-particle-type population quantity information further comprises utilizing the first-particle-type precision parameter and the first- particle-type subset quantity information to determine a second subset of the plurality of images to analyze, the second subset being larger in number than the first subset, and analyzing the second subset of the plurality of images.

6. The method of claim 5, further comprising determining a first-parti cl e-type modified subset quantity information from analyzing the second subset of the plurality of images, and utilizing the first-particle-type modified subset quantity information to determine the first-particle-type population quantity information.

7. The method of claim 6, wherein determining the first-particle-type population quantity information further comprises applying a scaling factor to the first-particle-type modified subset quantity information.

8. The method of claim 1, wherein the first-particle-type precision parameter corresponds to a predetermined value.

9. The method of claim 1, further comprising determining a second-particle-type subset quantity information for a second particle type from the first subset, obtaining a second- particle-type precision parameter, and determining a second-particle-type population quantity information for the second particle type by utilizing the second-particle-type precision parameter and the second-particle-type subset quantity information.

10. The method of claim 9, wherein the first particle type is one of an erythrocyte, a reticulocyte, a nucleated red blood cell, a platelet, or a white blood cell, and wherein the second particle type is a different one of an erythrocyte, a reticulocyte, a nucleated red blood cell, a platelet, or a white blood cell.

11. The method of claim 9, wherein the first-particle-type precision parameter and the second- particle-type precision parameter comprise different numbers.

12. The method of claim 1, wherein the first-particle-type subset quantity information is one of a count of the first particle type obtained from the first subset of images, or a number of images of the first subset of images containing the first particle type.

13. The method of claim 1, wherein the first-particle-type population quantity information is one of a cell count or a cell concentration.

14. A biological sample analyzer comprising: a camera configured to obtain a plurality of images of particles of a biological sample; a processor; and a computer-readable medium storing instructions that, when executed by the processor, cause the processor to: receive the plurality of images; determine a first-parti cl e-type subset quantity information for a first particle type from a first subset of the plurality of images; obtain a first-particle-type precision parameter; and utilize the first-particle-type subset quantity information and the first-particle-type precision parameter to determine a first-particle-type population quantity information for the first particle type.

15. The biological sample analyzer of claim 14, wherein the first particle type is one of an erythrocyte, a reticulocyte, a nucleated red blood cell, a platelet, or a white blood cell.

16. The biological sample analyzer of claim 14, wherein the first-particle-type precision parameter corresponds to a standard deviation of accuracy required for the first-particle-type population quantity information.

17. The biological sample analyzer of claim 14, wherein the first-particle-type precision parameter corresponds to a percentage of images in the first subset of the plurality of images that include instances of the first particle type.

18. The biological sample analyzer of claim 14, wherein the computer-readable medium storing instructions that, when executed by the processor further cause the processor to determine the first-particle-type population quantity information by utilizing the first-particle- type precision parameter and the first-particle-type subset quantity information to determine a second subset of the plurality of images to analyze, the second subset being larger in number than the first subset, and analyzing the second subset of the plurality of images.

19. The biological sample analyzer of claim 18, wherein the computer-readable medium storing instructions that, when executed by the processor further cause the processor to determine a first-particle-type modified subset quantity information from analyzing the second subset of the plurality of images, and utilize the first-particle-type modified subset quantity information to determine the first-particle-type population quantity information.

20. The biological sample analyzer of claim 19, wherein the computer-readable medium storing instructions that, when executed by the processor further cause the processor to determine the first-parti cl e-type population quantity information by applying a scaling factor to the first-particle-type modified subset quantity information.

21. The biological sample analyzer of claim 14, wherein the first-particle-type precision parameter corresponds to a predetermined value.

22. The biological sample analyzer of claim 14, wherein the computer-readable medium storing instructions that, when executed by the processor further cause the processor todetermine a second-particle-type subset quantity information for a second particle type from the first subset, obtain a second-particle-type precision parameter, and determine a second- particle-type population quantity information for the second particle type by utilizing the second-particle-type precision parameter and the second-particle-type subset quantity information.

23. The biological sample analyzer of claim 22, wherein the first particle type is one of an erythrocyte, a reticulocyte, a nucleated red blood cell, a platelet, or a white blood cell, and wherein the second particle type is a different one of an erythrocyte, a reticulocyte, a nucleated red blood cell, a platelet, or a white blood cell.

24. The biological sample analyzer of claim 22, wherein the first-particle-type precision parameter and the second-particle-type precision parameter comprise different numbers.

25. The biological sample analyzer of claim 14, wherein the first-particle-type subset quantity information is one of a count of the first particle type obtained from the first subset of images, or a number of images of the first subset of images containing the first particle type.

26. The biological sample analyzer of claim 14, wherein the first-particle-type population quantity information is one of a cell count or a cell concentration.

27. A computer-implemented method for analyzing a biological sample, comprising: receiving a plurality of images of the biological sample; determining a first-particle-type subset quantity information for a first particle type from a first subset of the plurality of images; utilizing the first-particle-type subset quantity information to determine a second subset of the plurality of images to analyze, the second subset being larger than the first subset; anddetermining a first-particle-type population quantity information for the first particle type from the second subset of the plurality of images.

28. The method of claim 27, wherein the first particle type is one of an erythrocyte, a reticulocyte, a nucleated red blood cell, a platelet, or a white blood cell.

29. The method of claim 27, further comprising determining a first-particle-type modified subset quantity information from analyzing the second subset of the plurality of images, and utilizing the first-particle-type modified subset quantity information to determine the first- particle-type population quantity information.

30. The method of claim 29, wherein determining the first-particle-type population quantity information further comprises applying a scaling factor to the first-particle-type modified subset quantity information.

31. The method of claim 29, wherein the first-parti cl e-type subset quantity information is one of a count of the first particle type obtained from the first subset of images, or a number of images of the first subset of images containing the first particle type.

32. The method of claim 29, wherein the first-parti cl e-type population quantity information is one of a cell count or a cell concentration.

33. A biological sample analyzer comprising: a camera configured to take a plurality of images of particles of a biological sample; a processor; anda computer-readable medium storing instructions that, when executed by a processor, causes the processor to: receive the plurality of images; determine a first-particle-type subset quantity information for a first particle type from a first subset of the plurality of images; utilize the first-particle-type quantity information to determine a second subset of the plurality of images to analyze, the second subset being larger than the first subset; and determine a first-parti cle-type population quantity information for the first particle type from the second subset of the plurality of images.

34. The biological sample analyzer of claim 33, wherein the first particle type is one of an erythrocyte, a reticulocyte, a nucleated red blood cell, a platelet, or a white blood cell.

35. The biological sample analyzer of claim 33, wherein the computer-readable medium storing instruction that, when executed by the processor, further cause the processor to determine a first-particle-type modified subset quantity information from analyzing the second subset of the plurality of images, and utilize the first-particle-type modified subset quantity information to determine the first-particle-type population quantity information.

36. The biological sample analyzer of claim 35, wherein the computer-readable medium storing instruction that, when executed by the processor, further cause the processor to determine the first-particle-type population quantity information by applying a scaling factor to the first-parti cle-type modified subset quantity information.

37. The biological sample analyzer of claim 35, wherein the first-particle-type subset quantity information is one of a count of the first particle type obtained from the first subset of images, or a number of images of the first subset of images containing the first particle type.

38. The biological sample analyzer of claim 35, wherein the first-particle-type population quantity information is one of a cell count or a cell concentration.

39. A computer-implemented method for analyzing a biological sample, comprising: receiving a plurality of images of a biological sample; analyzing a first subset of the plurality of images to determine a first-particle-type subset quantity information for a first particle type; determining a first-particle-type population quantity information for the first particle type by applying a scaling factor to the first-population-type subset quantity information.

40. A biological sample analyzer comprising: a camera configured to take a plurality of images of particles of the biological sample; a processor; and a computer-readable medium storing instructions that, when executed by a processor, cause the processor to: receive a plurality of images; determine a first-particle-type subset quantity information for a first particle type from a first subset of the plurality of images; and determine a first-parti cle-type population quantity information for the first particle type by applying a scaling factor to the first-particle-type quantity information.

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