Method and apparatus for visualization of bone marrow cell populations

Abstract

To provide methods and devices for visualization of bone marrow cell populations.SOLUTION: A microscope system for scanning a bone marrow aspirate (BMA) sample may include a scanning device for scanning a BMA sample and a processor coupled to the scanning device and a memory. The microscope system may obtain scan data of the BMA sample using the scanning device and detect cells in the BMA sample from the scan data. The microscope system may also classify the detected cells into a plurality of cell types and store cell data of the classified cells in a memory. The microscope system may include a display for presenting the cell data. Various other systems and methods are also provided.SELECTED DRAWING: Figure 7

Description

[Background technology]

[0001] (CROSS-REFERENCE TO RELATED APPLICATIONS) This application claims the benefit under 35 U.S.C. § 119(e) of U.S. Provisional Patent Application No. 62 / 935,843, filed November 15, 2019, and entitled "VISUALIZATION OF BONE MARROW CELL POPULATIONS," and U.S. Provisional Patent Application No. 62 / 705,501, filed June 30, 2020, and entitled "BONE MARROW ASPIRATE DIAGNOSTIC SYSTEM," both of which are incorporated by reference herein in their entireties.

[0002] Bone marrow aspirate analysis can be used in the diagnosis of many types of diseases, such as leukemia. However, research related to the present disclosure suggests that previous approaches to analyzing bone marrow samples may not be ideal, and improved analysis of bone marrow aspirate samples may help physicians make improved diagnoses for patients. Analysis of bone marrow aspirate ("BMA") samples can be a complex and time-consuming process, and at least some of the potentially available data from BMA samples is underutilized, which can lead to inaccurate analysis and diagnosis in at least some cases. For example, after preparing the sample, an operator may analyze the sample under a microscope, with at least part of the analysis being performed using high magnification with oil immersion having a small field of view. This approach can be time-consuming and may result in the operator not being able to examine all of the potentially relevant cells within a reasonable timeframe. Bone marrow sample analysis often relies on differential analysis of different cell types in the sample in different regions of the sample. Bone marrow sample analysis often involves analyzing the macrostructure of the sample at lower magnification to check its suitability and find an area for analysis, then searching for relevant cells at higher magnification and performing differential analysis on approximately 500 cells (according to ICSH guidelines). Research related to the present disclosure suggests that the sensitivity and specificity of previous BMA analysis may not be ideal, at least in some cases. For example, in some diseases, cells indicative of the presence of disease in a BMA sample may be present in a focal area of ​​the BMA sample, which may be overlooked in at least some cases.

[0003] Traditionally, analysis is performed manually by a person. The person may examine the sample under a microscope, select a relevant area of ​​the sample, identify various cell types within the relevant area, and based on the results (e.g., the number of cells per cell type), the person may attempt to diagnose the condition. Research related to the present disclosure suggests that previous approaches to determining the relevant area of ​​a sample identification of cell types may be somewhat subjective in at least some cases. In some previous approaches, the relevant area of ​​the sample may not be properly defined, and bias may be introduced into the analysis, which may affect the accuracy of the analysis in at least some cases.

[0004] Previous approaches may suffer from additional limitations. For example, because the analysis is performed manually, a person may rely on cognitive awareness and memory of the conditions present to assess the clinical significance of different cell populations. This may be exacerbated by the challenge of tracking many cells that are difficult to distinguish and indicator cells that may be abnormal or otherwise indicate a medical condition. In addition, some diseases may appear in specific parts of the sample, which may be overlooked. The process may also be lengthy, as a person may perform the analysis individually and repeat the entire process for multiple slides. Thus, traditional approaches may be prone to human error in at least some cases. Previous approaches are also somewhat limited because the types of analysis performed are somewhat limited by the cognitive abilities of the individual performing the analysis, and research related to the present disclosure suggests that relationships between cell types and lineages may not be properly utilized in at least some cases. Furthermore, because many healthcare facilities lack personnel with the necessary level of expertise, at least some facilities may rely on remote experts by physically transporting samples, further increasing overhead and delays for analyzing samples. Summary of the Invention [Means for solving the problem]

[0005] The systems and methods described herein may provide a computer-assisted system for bone marrow aspirate analysis, including visualization of bone marrow cell populations, that can be used by healthcare providers to perform diagnoses. In some embodiments, the disclosed systems and methods enable quantitative analysis of a large number of cells over a large area, which can increase the sensitivity and specificity of BMA analysis. Additionally, the area for analysis can be selected in a manner that reduces bias in the analysis. While the area for analysis can be selected in many ways, in some embodiments, an area for analysis that is representative of other areas is selected. Alternatively, or in combination, cells from other areas that are representative of cells in a given area are selected for analysis, which can improve the reliability of the area selected for analysis and reduce bias.

[0006] In some embodiments, scan data from a BMA sample is received. One or more particles or cell locations are detected from the scan data, and an analysis area is determined based on the one or more detected particle or cell locations. Cells of one or more cell types can be identified from the scan data. The identified cells can be analyzed using statistical analysis, and a subset of the identified cells can be selected for presentation based on the statistical analysis, which can facilitate diagnosis by a healthcare provider.

[0007] In some embodiments, a region of interest is quantitatively determined based on the particle and cell density of the sample, which can reduce bias in BMA analysis. Representative cells within the region of interest can also be selected. Different cell populations may be detected and presented in a manner that can facilitate diagnosis of normal or abnormal cell populations. The visual representation may use color, size, shape, text / numbers, and / or other visual indicators to indicate the lineage relationships between cell types, the number or percentage of different cell types, and the amount relative to normal range values. The visual representation may advantageously facilitate rapid diagnosis of abnormalities, communicate and track decision-making, and enable comparison with an atlas of known conditions (e.g., graphs corresponding to known conditions) to reduce human error, improve the consistency and quality of diagnoses, and enable rapid results. The present invention provides, for example, the following items. (Item 1) 1. A system for scanning a bone marrow aspirate (BMA) sample, the system comprising: a scanning device for scanning the BMA sample; a processor coupled to the scanning device and a memory, the processor providing the system with: acquiring scan data of the BMA sample using the scanning device; detecting cells in the BMA sample from the scanned data; classifying the detected cells into a plurality of cell types; storing cell data of the classified cells in the memory; a processor configured to execute instructions to cause a display configured to present the cellular data; and A system comprising: (Item 2) Item 10. The system of item 1, wherein the processor is configured to detect particles in the BMA sample and detect cells in the BMA sample separate from the detected particles. (Item 3) 3. The system of claim 2, wherein the particles comprise one or more clusters of adipose or hematopoietic cells. (Item 4) further comprising an image capture device with an effective numerical aperture (NA) of at least 0.8; the detected cells include at least 100 cells; the instructions further comprise instructions for scanning an area of ​​at least 0.5 square centimeters. Item 1. The system of item 1. (Item 5) 2. The system of claim 1, wherein classifying the detected cells further comprises identifying a cell lineage that connects cells of different cell types, and presenting the cell data further comprises presenting the cell lineage. (Item 6) 6. The system of claim 5, wherein the cell lineage comprises the lineage of the cell from one or more of the myeloid, lymphoid, or erythroid lineages. (Item 7) 2. The system of claim 1, wherein storing the cellular data further comprises selecting at least relevant cells from the detected cells to be stored in the cellular data. (Item 8) 8. The system of claim 7, wherein the relevant cells are selected based on a representative distribution of cell types from the detected cells. (Item 9) 8. The system of claim 7, wherein the number of relevant cells is 100 to 500 cells, 500 to 1,000 cells, 1,000 to 2,000 cells, or more than 2,000 cells. (Item 10) Item 1. The system of item 1, wherein the scanning time for acquiring the scan data is faster than 20 minutes per square cm, 15 minutes per square cm, 10 minutes per square cm, 5 minutes per square cm, or 2 minutes per square cm. (Item 11) 2. The system of claim 1, wherein the scanned data comprises data from at least 95% of the entire area of ​​the BMA sample. (Item 12) The instructions further include: analyzing a preview image corresponding to the BMA sample to locate particles, the preview image having a lower resolution than the scanned data; determining a recommended scan area for the BMA sample based on the analysis; Item 1. The system of item 1, comprising instructions for causing the system to: (Item 13) Item 13. The system of item 12, wherein the recommended scan area is further determined based on user input. (Item 14) Item 14. The system of item 13, wherein the user input includes adjusting the recommended scan area to include or exclude a user-defined area. (Item 15) Item 13. The system of item 12, wherein the instructions for acquiring scan data further comprise instructions for scanning the recommended scan area using the scanning device. (Item 16) 2. The system of claim 1, wherein the instructions further comprise instructions for receiving user input to modify the cell data. (Item 17) 17. The system of claim 16, wherein the user input comprises at least one of marking a cell, adding a cell, deleting a cell, reclassifying a cell, or adding a notification to a cell. (Item 18) 18. The system of claim 17, wherein the notification indicates at least one of toxicity or abnormality. (Item 19) 2. The system of claim 1, wherein the scan data includes high-resolution image data of the BMA sample. (Item 20) 20. The system of claim 19, wherein the high-resolution image data comprises image data of the BMA sample scanned using an effective numerical aperture (NA) of at least 0.8. (Item 21) 20. The system of claim 19, wherein the high-resolution image data comprises image data of the BMA sample scanned using an effective numerical aperture (NA) of at least 0.9. (Item 22) 20. The system of claim 19, wherein the high-resolution image data comprises image data of the BMA sample scanned using an effective numerical aperture (NA) of at least 1.0. (Item 23) 2. The system of claim 1, wherein the scanned data comprises at least 0.5 square cm of the BMA sample, at least 1 square cm of the BMA sample, at least 1.5 square cm of the BMA sample, or at least 2 square cm of the BMA sample. (Item 24) 2. The system of claim 1, wherein the number of cells detected is at least one of 500 or more cells, 1,000 or more cells, 2,000 or more cells, 5,000 or more cells, or 10,000 or more cells. (Item 25) 2. The system of claim 1, wherein the number of cells classified is at least one of 500 or more cells, 1,000 or more cells, 2,000 or more cells, 5,000 or more cells, or 10,000 or more cells. (Item 26) Item 10. The system of item 1, wherein the processor is configured to classify the cells using a machine learning classifier, the machine learning classifier comprising one or more models selected from the group consisting of neural networks, convolutional neural networks, decision trees, support vector machines, regression analysis, Bayesian networks, and training models. (Item 27) 27. The system of claim 26, wherein the machine learning classifier is configured to identify a plurality of at least ten cell types selected from the group consisting of blasts, immature eosinophils, eosinophils, immature basophils, basophils, promyelocytes, myelocytes, metamyelocytes, bands, neutrophils, monoblasts, monocytes, macrophages, megakaryoblasts, megakaryocytes, erythroblasts, megaloblasts, normoblasts, lymphocytes, and plasma cells. (Item 28) The scanning device is an illumination assembly configured to illuminate the BMA sample at a plurality of angles; an image capture device configured to collect a plurality of images from the BMA sample illuminated by the illumination assembly at the plurality of angles, the plurality of images comprising a first minimum resolvable distance for the cellular feature; and Equipped with Item 10. The system of item 1, wherein the processor is operatively coupled to the illumination assembly and the image capture device, and the processor is configured with instructions for generating a computationally reconstructed image from the plurality of images, the computationally reconstructed image comprising a second minimum resolvable distance for features of the cell, the second minimum resolvable distance being less than the first minimum resolvable distance. (Item 29) Item 10. The system of item 1, wherein the processor is configured to display a cell density heatmap. (Item 30) 2. The system of claim 1, wherein the processor is configured with instructions for identifying intact cells and determining an area for further analysis based on the location of the intact cells. (Item 31) 1. A system for scanning a bone marrow aspirate (BMA) sample, comprising: a processor coupled to a memory, the processor providing the system with: receiving scan data of the BMA sample; detecting particles in the BMA sample from the scanned data; and detecting cells in the BMA sample separate from the detected particles; determining a region of interest (ROI) based on the detected cells; selecting a subset of the detected cells for cellular data based in part on the ROI and the detected cells; storing the cell data in the memory; a processor configured to execute instructions to cause a display configured to present the cellular data; and A system comprising: (Item 32) 32. The system of claim 31, wherein detecting the cells further comprises detecting intact cells, and the ROI is further determined based on the detected intact cells. (Item 33) 32. The system of claim 31, wherein detecting the cells further comprises detecting a cell density, and wherein the ROI is further determined based on the cell density. (Item 34) 32. The system of claim 31, wherein the ROI is further determined based on a distance between the detected cell and the detected particle. (Item 35) 32. The system of claim 31, wherein the ROI is further determined based on associated localization information corresponding to one or more specific locations of the BMA sample. (Item 36) 32. The system of claim 31, wherein the associated localization information corresponds to a localized disease with features in the one or more specific locations. (Item 37) 37. The system of claim 36, wherein the localized disease comprises one or more of lymphoma, plasma cell myeloma, mastocytosis, metastatic carcinoma, storage histiocytosis, crystal storage histiocytosis, or granuloma. (Item 38) 32. The system of claim 31, wherein detecting the cells further comprises classifying the cell type of the cells. (Item 39) 32. The system of claim 31, wherein the ROI comprises a plurality of ROIs, each of the plurality of ROIs comprising a distribution of cells, and wherein a subset of cells is selected from the subset of the plurality of ROIs based on a comparison of the collective distribution of cells from the plurality of ROIs to a total distribution of cells from the ROI. (Item 40) 40. The system of claim 39, wherein the subset of cells is further selected based on having a cell distribution of a cell type that represents a total cell distribution of a cell type in the scanned data. (Item 41) 40. The system of claim 39, wherein the subset of cells is further selected based on prioritizing one or more cell types for inclusion in the cellular data. (Item 42) 42. The system of claim 41, wherein the prioritized one or more cell types comprise blast cells or plasma cells. (Item 43) 32. The system of claim 31, wherein presenting the cellular data includes presenting a comparison of cell types of the first set of cells and the second set of cells, optionally wherein the comparison comprises a ratio. (Item 44) Item 44. The system of item 43, wherein the first set of cells corresponds to a subset of the selected cells. (Item 45) 44. The system of claim 43, wherein the second set of cells corresponds to a general population of cells from the BMA sample. (Item 46) 32. The system of claim 31, wherein the processor is further configured to edit the cell data based on user input. (Item 47) 32. The system of claim 31, wherein the processor is configured to display a cell density heatmap. (Item 48) 1. A system for scanning a bone marrow aspirate (BMA) sample, comprising: a processor coupled to a memory, the processor providing the system with: receiving scan data of the BMA sample; detecting cells in the BMA sample; classifying the detected cells into one or more cell types; For each of the one or more cell types, determining a percentage of the cell type relative to the set of detected cells for the cellular data; storing the cell data in the memory; a processor configured to execute instructions to cause a display configured to present the cellular data based on the lineage of the one or more cell types; and A system comprising: (Item 49) 49. The system of claim 48, wherein the set of detected cells corresponds to a general population of the detected cells or a selected subset of the detected cells. (Item 50) 49. The system of claim 48, wherein the cellular data comprises a percentage of abnormal cells for each of the one or more cell types. (Item 51) 49. The system of claim 48, wherein the cellular data includes a statistical analysis for each of the one or more cell types. (Item 52) 52. The system of claim 51, wherein the statistical analysis includes at least one of a distribution, a mean, or a median. (Item 53) Item 49. The system of item 48, wherein presenting the cellular data comprises a graphical presentation of the lineage of the one or more cell types. (Item 54) 54. The system of claim 53, wherein the graphical presentation includes, for each of the one or more cell types, a visual comparison of the percentage of the cell type relative to the set of detected cells or to a subset of the set of detected cells. (Item 55) 55. The system of claim 54, wherein the processor is configured with instructions for receiving user input to select a subset of the detected cells. (Item 56) 55. The system of claim 54, wherein the visual comparison comprises, for each of the one or more cell types, a shape that is scaled based on the percentage of the cell type. (Item 57) 54. The system of claim 53, wherein the graphical presentation includes a visual comparison based on a range of values ​​for each of the one or more cell types. (Item 58) 54. The system of claim 53, wherein the graphical representation comprises one or more colors corresponding to one or more cellular properties. (Item 59) Item 49. The system of item 48, wherein presenting the cellular data includes presenting a cell density heat map. (Item 60) 49. The system of claim 48, wherein the lineage comprises the lineage of the cell from one or more of the myeloid, lymphoid, or erythroid lineages. (Item 61) 1. A system for scanning a bone marrow aspirate (BMA) sample, the system comprising: a processor coupled to a memory, the processor providing the system with: receiving scan data from the BMA sample; detecting one or more of particle or cell locations from the scanned data; determining an analysis area based on one or more of the detected particle or cell locations; identifying cells of one or more cell types from the scanned data; and analyzing the identified cells using statistical analysis; selecting a subset of the identified cells based on the statistical analysis; a processor configured to execute instructions to cause A system comprising: (Item 62) 62. The system of claim 61, wherein determining the analysis area further comprises determining an associated location based on identifying intact cells. (Item 63) Item 62. The system of item 61, wherein the statistical analysis is based on the location of the identified cells. (Item 64) 62. The system of claim 61, wherein the statistical analysis is based on comparing cell properties to a general population of the identified cells. (Item 65) Item 62. The system of item 61, further comprising adjusting the statistical analysis based on user input. (Item 66) Item 66. The system of item 65, wherein the user input includes adjusting the recommended scan area to include or exclude a user-defined area. (Item 67) 62. The system of claim 61, wherein the second subset of identified cells comprises randomly selected cells of one or more cell types not selected for the identified subset of cells. (Item 68) Item 68. The system of item 67, wherein the processor is configured with instructions for presenting data from the second subset to a user and detecting bias in the subset.

[0008] (Incorporated by reference) All patents, applications, and publications referenced and identified herein are incorporated herein by reference in their entirety and shall be considered to be incorporated by reference in their entirety even if referenced elsewhere in this application. [Brief explanation of the drawings]

[0009] A better understanding of the features, advantages, and principles of the present disclosure will be obtained by reference to the following detailed description that sets forth illustrative embodiments and the accompanying drawings.

[0010] [Figure 1] FIG. 1 shows a schematic diagram of an exemplary microscope, according to some embodiments.

[0011] [Figure 2] FIG. 2 shows a chart of cell lineages, according to some embodiments.

[0012] [Figure 3] FIG. 3 shows another chart of cell lineages, according to some embodiments.

[0013] [Figure 4] FIG. 4 shows an exemplary cell lineage analysis, according to some embodiments.

[0014] [Figure 5] FIG. 5 shows an exemplary scanning analysis of a sample, according to some embodiments.

[0015] [Figure 6] FIG. 6 shows an exemplary heat map of cell density, according to some embodiments.

[0016] [Figure 7] FIG. 7 shows a flowchart of an exemplary process for bone marrow scan analysis in classifying cells, according to some embodiments.

[0017] [Figure 8] FIG. 8 shows a flowchart of an exemplary process for bone marrow scan analysis in selecting representative cells, according to some embodiments.

[0018] [Figure 9] FIG. 9 shows a flowchart of an exemplary process for bone marrow scan analysis of cell population data, according to some embodiments.

[0019] [Figure 10] FIG. 10 shows a flowchart of an exemplary process for bone marrow scan analysis in selecting representative cells, according to some embodiments.

[0020] [Figure 11] FIG. 11 shows a flowchart of an exemplary process for reviewing a scan, according to some embodiments.

[0021] [Figure 12] FIG. 12 shows a flowchart of an exemplary process for analyzing a scan, according to some embodiments.

[0022] [Figure 13] FIG. 13 shows a flowchart of an exemplary process for bone marrow scan analysis, according to some embodiments.

[0023] [Figure 14] FIG. 14 illustrates an exemplary computing system, according to some embodiments.

[0024] [Figure 15] FIG. 15 illustrates an exemplary network architecture, according to some embodiments. DETAILED DESCRIPTION OF THE INVENTION

[0025] Detailed Description The following detailed description provides a deeper understanding of the features and advantages of the invention described in this disclosure, in accordance with the embodiments disclosed herein. Although the detailed description includes many specific embodiments, these are provided by way of example only and should not be construed as limiting the scope of the invention disclosed herein.

[0026] The following will provide a detailed description of the analysis of BMAs with reference to Figures 1-15. Figure 1 illustrates a microscope and various microscope configurations. Figures 2, 3, and 4 illustrate exemplary charts of cell lineage analysis of a sample. Figures 5 and 6 illustrate exemplary screenshots of scan results. Figures 7, 8, 9, 10, 11, 12, and 13 illustrate a flow chart of an exemplary process for bone marrow aspirate sample analysis. Figure 14 illustrates an exemplary computing device capable of bone marrow aspirate sample analysis as described herein. Figure 15 illustrates an exemplary networking architecture for a computing device described herein.

[0027] The disclosed systems and methods are well suited for use in conjunction with previous methods of collecting bone marrow aspirate samples. Bone marrow aspirate samples can be obtained in a manner that would typically be performed as would be understood by one of ordinary skill in the art. Bone marrow aspirate samples can be obtained from any species of human or animal that has bone marrow, and samples can be obtained from living subjects, such as patients, or deceased subjects, such as cadavers.

[0028] FIG. 1 is a diagrammatic representation of a microscope 100 consistent with exemplary disclosed embodiments. As used herein, the term “microscope” generally refers to any device or instrument for magnifying objects smaller than those readily observable by the naked eye, i.e., creating an image for a user of an object whose image is larger than the object. One type of microscope may be an “optical microscope,” which uses light in combination with an optical system to magnify the object. An optical microscope may be a single microscope with one or more magnifying lenses. Another type of microscope may be a “computational microscope,” which includes an image sensor and image processing algorithms to enhance or magnify the size or other properties of an object. Computational microscopes may be dedicated devices or may be created by incorporating software and / or hardware into an existing optical microscope to generate high-resolution digital images. As shown in FIG. 1 , the microscope 100 includes an image capture device 102, a focus actuator 104, a controller 106 connected to a memory 108, an illumination assembly 110, and a user interface 112. An exemplary use of the microscope 100 may be to capture an image of a sample 114 mounted on a stage 116 located within the field of view (FOV) of the image capture device 102, process the captured image, and present a magnified image of the sample 114 on the user interface 112.

[0029] The image capture device 102 may be used to capture images of the sample 114. As used herein, the term “image capture device” generally refers to a device that records an optical signal incident on a lens as an image or a sequence of images. The optical signal may be in the near-infrared, infrared, visible, and ultraviolet spectrums. Examples of image capture devices include CCD cameras, CMOS cameras, color cameras, photosensor arrays, video cameras, camera-equipped mobile phones, webcams, preview cameras, microscope objective lenses and detectors, and the like. Some embodiments may include only a single image capture device 102, while other embodiments may include two, three, or even four or more image capture devices 102. In some embodiments, the image capture device 102 may be configured to capture images within a defined field of view (FOV). Also, when the microscope 100 includes several image capture devices 102, the image capture devices 102 may have overlapping areas within their respective FOVs. Image capture device 102 may have one or more image sensors (not shown in FIG. 1 ) for capturing image data of sample 114. In other embodiments, image capture device 102 may be configured to capture images at an image resolution greater than VGA, greater than 1 megapixel, greater than 2 megapixels, greater than 5 megapixels, greater than 10 megapixels, greater than 12 megapixels, greater than 15 megapixels, or greater than 20 megapixels. Additionally, image capture device 102 may also be configured to have a pixel size less than 15 micrometers, less than 10 micrometers, less than 5 micrometers, less than 3 micrometers, or less than 1.6 micrometers.

[0030] In some embodiments, the microscope 100 includes a focus actuator 104. As used herein, the term “focus actuator” generally refers to any device capable of converting an input signal into physical motion for adjusting the relative distance between the sample 114 and the image capture device 102. Various focus actuators may be used, including, for example, linear motors, electrostrictive actuators, electrostatic motors, capacitive motors, voice coil actuators, magnetostrictive actuators, etc. In some embodiments, the focus actuator 104 may include analog position feedback sensors and / or digital position feedback elements. The focus actuator 104 is configured to receive commands from the controller 106 to focus the light beam and form a clear, sharply defined image of the sample 114. In the example illustrated in FIG. 1 , the focus actuator 104 may be configured to adjust the distance by moving the image capture device 102.

[0031] However, in other embodiments, the focus actuator 104 may be configured to adjust the distance by moving the stage 116, or by moving both the image capture device 102 and the stage 116. The microscope 100 may also include a controller 106 for controlling the operation of the microscope 100 according to disclosed embodiments. The controller 106 may include various types of devices for performing logical operations on one or more inputs of image data and other data according to stored or accessible software instructions that provide the desired functionality. For example, the controller 106 may include a central processing unit (CPU), support circuits, a digital signal processor, an integrated circuit, cache memory, or any other type of device for image processing and analysis, such as a graphics processing unit (GPU). The CPU may include any number of microcontrollers or microprocessors configured to process imagery from the image sensor. For example, the CPU may include any type of single- or multi-core processor, a mobile device microcontroller, or the like. A variety of processors may be used, including, for example, processors available from manufacturers such as Intel®, AMD®, etc., and may have a variety of architectures (e.g., x86 processors, ARM®, etc.). The support circuits may generally be any number of circuits known in the art, including cache, power supplies, clocks, and input / output circuits. The controller 106 may be in a remote location, such as a computing device communicatively coupled to the microscope 100.

[0032] In some embodiments, the controller 106 may be associated with memory 108 that is used to store software that, when executed by the controller 106, controls the operation of the microscope 100. In addition, the memory 108 may also store electronic data associated with the operation of the microscope 100, such as, for example, captured or generated images of the sample 114. In one case, the memory 108 may be integrated into the controller 106. In another case, the memory 108 may be separate from the controller 106.

[0033] Specifically, memory 108 may refer to multiple structures or computer-readable storage media located on controller 106 or at a remote location, such as a cloud server. Memory 108 may comprise any number of random access memory, read-only memory, flash memory, disk drives, optical storage devices, tape storage devices, removable storage devices, and other types of storage devices.

[0034] The microscope 100 may include an illumination assembly 110. As used herein, the term "illumination assembly" generally refers to any device or system capable of projecting light to illuminate a sample 114.

[0035] The illumination assembly 110 may include any number of light sources, such as light emitting diodes (LEDs), LED arrays, lasers, and lamps configured to emit light, such as halogen lamps, incandescent lamps, or sodium lamps. For example, the illumination assembly 110 may include a Kohler illumination source. The illumination assembly 110 may be configured to emit polychromatic light. For example, the polychromatic light may include white light.

[0036] In some embodiments, the illumination assembly 110 may include only a single light source. Alternatively, the illumination assembly 110 may include 4, 16, or even more than 100 light sources organized in an array or matrix. In some embodiments, the illumination assembly 110 may use one or more light sources located in a parallel surface to illuminate the sample 114. In other embodiments, the illumination assembly 110 may use one or more light sources located in a surface perpendicular to the sample 114 or at an angle thereto.

[0037] Additionally, the illumination assembly 110 may be configured to illuminate the sample 114 under a series of different illumination conditions. In one example, the illumination assembly 110 may include multiple light sources arranged at different illumination angles, such as a two-dimensional array of light sources. In this case, the different illumination conditions may include different illumination angles. For example, FIG. 1 depicts a beam 118 projected from a first illumination angle α1 and a beam 120 projected from a second illumination angle α2. In some embodiments, the first illumination angle α1 and the second illumination angle α2 may have the same value but opposite signs. In other embodiments, the first illumination angle α1 may be distinct from the second illumination angle α2. However, both angles originate from a point within the acceptance angle of the optical system. In another example, the illumination assembly 110 may include multiple light sources configured to emit light at different wavelengths. In this case, the different illumination conditions may include different wavelengths. For example, each light source may be configured to emit light with a full-width half-maximum bandwidth of 50 nm or less to emit substantially monochromatic light. In yet another embodiment, the illumination assembly 110 may be configured to use several light sources at a given time. In this case, the different illumination conditions may comprise different illumination patterns. For example, the light sources may be arranged to sequentially illuminate the sample at different angles to provide one or more of digital refocusing, aberration correction, or resolution enhancement. Thus, consistent with the present disclosure, the different illumination conditions may be selected from a group including different durations, different intensities, different positions, different illumination angles, different illumination patterns, different wavelengths, or any combination thereof.

[0038] Although computational microscopy is mentioned, the disclosed systems and methods are well suited for use with many types of microscopy and microscopes, such as one or more of high-resolution microscopes, digital microscopes, scanning digital microscopes, 3D microscopes, phase imaging microscopes, phase contrast microscopes, dark field microscopes, differential interference contrast microscopes, light sheet microscopes, confocal microscopes, holographic microscopes, or fluorescence-based microscopes.

[0039] Consistent with disclosed embodiments, the microscope 100 may include, be connected to, or communicate (e.g., via a network or wirelessly, e.g., via Bluetooth®) with a user interface 112. The term “user interface” as used herein generally refers to any device suitable for presenting a magnified image of the sample 114 or for receiving input from one or more users of the microscope 100. FIG. 1 illustrates two examples of the user interface 112. The first example is a smartphone or tablet that communicates wirelessly with the controller 106 via Bluetooth®, a cellular connection, or a Wi-Fi connection, either directly or through a remote server. The second example is a PC display physically connected to the controller 106. In some embodiments, the user interface 112 may include user output devices, including, for example, a display, a haptic device, a speaker, etc. In other embodiments, the user interface 112 may include user input devices, including, for example, a touchscreen, a microphone, a keyboard, a pointer device, a camera, a knob, a button, etc. Using such input devices, a user may be able to provide information input or commands to microscope 100 by typing instructions or information, providing voice commands, selecting on-screen menu options using buttons, a pointer, or eye-tracking capabilities, or through any other suitable technique for communicating information to microscope 100. User interface 112 may be coupled (physically or wirelessly) to one or more processing devices, such as controller 106, to provide and receive information to or from a user and process that information. In some embodiments, such processing devices may execute instructions for responding to keyboard strokes or menu selections, recognizing and interpreting touches and / or gestures made on a touchscreen, recognizing and tracking eye movements, receiving and interpreting voice commands, etc.

[0040] The microscope 100 may also include or be connected to a stage 116. The stage 116 comprises any horizontal, rigid surface onto which the sample 114 can be mounted for inspection. The stage 116 may include a mechanical connector for holding the slide containing the sample 114 in a fixed position. The mechanical connector may use one or more of the following: a mount, a mounting member, a holding arm, a clamp, a clip, an adjustable frame, a locking mechanism, a spring, or any combination thereof. In some embodiments, the stage 116 may include a translucent portion or opening to allow light to illuminate the sample 114. For example, light transmitted from the illumination assembly 110 may pass through the sample 114 and toward the image capture device 102. In some embodiments, the stage 116 and / or the sample 114 may be moved using motors or manual controls in the XY plane to enable imaging of multiple areas of the sample.

[0041] 2 illustrates an exemplary chart 200 of the classification of cells in a sample, which may correspond to a normal or near-normal bone marrow sample. As will be further described below, microscope 100 may scan and further analyze the bone marrow aspirate sample. Some of the results of the analysis may be presented visually in a chart, such as chart 200. For example, microscope 100 may classify cells identified in the sample, which may include, for example, blasts, immature eosinophils, eosinophils, immature basophils, basophils, promyelocytes, myelocytes, metamyelocytes, bands, neutrophils, monoblasts, monocytes, macrophages, megakaryoblasts, megakaryocytes, erythroblasts, megaloblasts, normoblasts, lymphocytes, plasma cells, etc.

[0042] Chart 200 may organize cell types based on lineage. For example, as seen in FIG. 2, different types of cells may be organized into columns by type with maturation relationships. In addition, each column may include more granular cell progression (e.g., immature eosinophils to eosinophils, etc.). Certain cell types, such as lymphocytes, may be displayed separately, as seen in FIG. 2.

[0043] Additionally, chart 200 may visually represent cell populations to reflect relative and / or absolute cell populations. In FIG. 2, the size of the circle corresponding to each cell type may indicate the population. For example, larger circles may indicate larger populations. The sizes may correspond to the percentage of the cell population corresponding to the individual cell type, or may scale directly based on the amount of cells. Additionally, the relative sizes between different circles may provide a visual estimate of the relative sizes of the corresponding cell populations. Although not shown in FIG. 2, additional text, such as numerical values, may be displayed as well.

[0044] Additional characteristics may be represented visually. Colors or other markings may represent normal and / or abnormal ranges or results. For example, characteristics may be represented by lines or dashed lines that may mark transitions from normal to abnormal, changes in line thickness connecting different cell types, changes in shape (e.g., using squares), changes in location within a chart, bars, other chart shapes, etc.

[0045] 2 shows different cell types with a neutral color (e.g., gray) to indicate the normal range. In other examples, other colors, such as green, may also be used to indicate the absence of a normal range or abnormal range.

[0046] FIG. 3 shows a chart 300 of a cellular analysis similar to chart 200, except depicting different results. Chart 300 may illustrate an abnormal bone marrow sample. For example, chart 300 may illustrate plasma cell abnormalities as indicated by the corresponding circle size as well as the circle color. In FIG. 3, the plasma cells may be represented by a dark color indicating an abnormality or otherwise calling for attention. In other examples, the abnormality may be illustrated in a warning color, such as red. Such a visual indicator may advantageously provide a simple and intuitive representation of the abnormality.

[0047] FIG. 4 illustrates an exemplary chart 400 of another visual presentation of cell lineages. While charts 200 and 300 present various cell lineages (e.g., broad cell lineage mapping), chart 400 may focus on a specific lineage (e.g., a column from charts 200 and 300). Similar to charts 200 and 300, chart 400 may display cell populations as circles with the size of the circle corresponding to the cell population, although in other examples, different visual indicators may be used. Above the circle, text and / or numbers may present the percentage of cells of that type relative to all nucleated cells, as well as the percentage of abnormal cells of that type (which may be manually marked by the user). In some examples, the text may be displayed in different colors, e.g., a neutral color for the percentage of all nucleated cells, and a red or warning color for abnormal cells. Below the circle, ranges may be displayed indicating the average area (e.g., μm ) between normal area values. 2 Aggregated results may be presented, such as the area covered by that type of cell, measured in units of area. As can be seen in chart 400, none of the average area values ​​fall outside their respective normal ranges. Below the ranges, cell types may be labeled, which may be presented in order of lineage progression, as further indicated by the arrows.

[0048] Charts 200, 300, and 400 show specific example arrangements of visual indicators and data. In other examples, the charts may vary, for example, with different arrangements, different colors / symbols, different data, etc.

[0049] FIG. 5 illustrates an exemplary screen 500 of a bone marrow aspirate sample analysis interface. As seen in FIG. 5, screen 500 shows image data 502 obtained from scanning an area of ​​a sample. Visual indicators, such as colors, may indicate different information. For example, darker areas may correspond to particles detected within the area. To indicate elements detected in the sample, certain information may be overlaid over the image data, including boundaries 504 and 506, which may be presented using different colors, line thicknesses, or other visual distinctions to distinguish them from one another. Boundary 504, illustrated using a darker line, may outline an area that has been computer-analyzed (e.g., to detect particles, cells, etc.). Boundary 506, illustrated using a lighter line, may outline a potential area or region of interest (ROI), which may be automatically determined based on computer analysis, user-selected, etc. In other examples, other areas may be outlined using other boundaries.

[0050] Screen 500 may include a thumbnail 508 that shows a preview image of the entire slide or a significant portion of the slide. Thumbnail 508 may be captured at a lower resolution and may cover a larger area than that of image data 502. Thumbnail 508 may include a boundary 510 that indicates the area of ​​the sample to which image data 502 corresponds.

[0051] In some embodiments, screen 500 may include additional information displayed graphically or textually. For example, statistical data (e.g., total number of cells detected, total number of intact cells detected, total number of detached cells detected, total number of particles detected, percentage cellularity, etc.) or other analytical results may be presented. Additionally, user interface elements may be presented, such as controls for changing the displayed data, adjusting or annotating the data, selecting a different area or sample, continuing the workflow, navigating to a different area, changing views, menus, etc.

[0052] FIG. 6 illustrates an exemplary screen 600 of a bone marrow aspirate sample analysis interface. As seen in FIG. 6, the screen 600 shows image data 602 obtained from scanning an area of ​​a sample. The image data 602 may comprise a cellular heat map showing the cell density of different cell types. Visual indicators, such as colors, may indicate different information. For example, different colors may correspond to different types of cells. Groupings of colored dots may indicate cell density. Certain information may be overlaid over the image data, including highlights 604, which may be presented using different colors or other visual distinctions to distinguish them from other highlights and visual indicators. The highlights 604, illustrated using light gray shading over dense cell groupings, may indicate areas selected for further analysis, such as areas the system may suggest for nucleated differential cell count ("NDC") analysis. In other examples, different highlights may be used to indicate different cell groupings.

[0053] Screen 600 may include a thumbnail 608 that shows a preview image of the entire slide or a significant portion of the slide. Thumbnail 608 may be captured at a lower resolution and may cover a larger area than that of image data 602. Thumbnail 608 may include a border 610 that indicates the area of ​​the sample to which image data 602 corresponds.

[0054] In some embodiments, screen 600 may include additional information displayed graphically or textually. For example, analysis results (e.g., the number of cells of each detected type) or other statistical data may be presented. In addition, user interface elements such as controls for changing the displayed data, changing the displayed highlights, adjusting or annotating the data, selecting a different sample, continuing the workflow, navigating to a different area, changing views, menus, etc. may be presented.

[0055] 7 illustrates a flowchart of an exemplary method 700 for presenting a bone marrow aspirate analysis. In one example, each of the steps shown in FIG. 7 may represent an algorithm whose structure includes and / or is represented by multiple substeps, an example of which will be provided in more detail below. The steps shown in FIG. 7 may be performed by one or more of the systems and / or portions therein described herein, such as microscope 100 and / or portions therein (e.g., controller 106).

[0056] 7, one or more of the systems described herein may use a scanning device to acquire scan data of a bone marrow aspirate ("BMA") sample in step 710. For example, microscope 100 may use image capture device 102 to acquire scan data of the BMA sample.

[0057] In some examples, image capture device 102 may have an effective numerical aperture ("NA") of at least 0.8. In some embodiments, the effective NA corresponds to the resolving power of a microscope having the same resolving power as an objective lens with that NA. Image capture device 102 may also have an objective lens with a magnification suitable to provide the effective NA, although the NA of the objective lens may be less than the effective NA of the microscope.

[0058] In some embodiments, acquiring the scan data may include scanning an area of ​​at least 0.5 square cm. For example, the scan data may include at least 0.5 square cm of the BMA sample, at least 1 square cm of the BMA sample, at least 1.5 square cm of the BMA sample, or at least 2 square cm of the BMA sample. The scan time for acquiring the scan data may be at least 20 minutes per square cm, 15 minutes per square cm, 10 minutes per square cm, 5 minutes per square cm, or faster than 2 minutes per square cm. The scan data may include data from at least 95% of the entire area of ​​the BMA sample.

[0059] In some embodiments, the scan data includes high-resolution image data of the BMA sample, which may include image data of the BMA sample scanned with an effective NA of at least 0.8, image data of the BMA sample scanned with an effective NA of at least 0.9, and / or image data of the BMA sample scanned with an effective NA of at least 1.0.

[0060] In some examples, the microscope 100 may analyze a preview image corresponding to the BMA sample to locate particles. The preview image may have a lower resolution than the scan data. Based on the analysis, the microscope 100 may determine a recommended scan area for the BMA sample. While the preview image can be obtained in many ways, in some embodiments, the preview image is obtained using a camera without a microscope objective. For example, the preview image can be obtained using a macro lens or other suitable camera lens without relying on a microscope objective to obtain the preview image.

[0061] In some examples, the recommended scan area may further be determined based on user input, which may include adjusting the recommended scan area to include or exclude a user-defined area.

[0062] In some embodiments, acquiring the scan data may further include scanning the recommended scan area using a scanning device. For example, after the recommended scan area is determined, the microscope 100 may acquire the scan data by scanning the recommended scan area using the image capture device 102.

[0063] In step 720, one or more of the systems described herein may detect cells in the BMA sample from the scan data. For example, microscope 100 may detect cells in the BMA sample from the scan data acquired in step 710. Microscope 100 may also use other artificial intelligence ("AI"), such as, for example, computer vision or machine learning. In some embodiments, the AI ​​comprises image recognition processor instructions configured to detect cells and identify cell types.

[0064] In some embodiments, the microscope 100 may detect particles in the BMA sample and detect cells in the BMA sample separate from the detected particles. The particles may include, for example, spicules or fragments, or one or more clusters of fat or hematopoietic cells. In some embodiments, the detected cells include at least 100 cells. For example, the number of detected cells is at least 500 or more cells, 1,000 or more cells, 2,000 or more cells, 5,000 or more cells, or 10,000 or more cells.

[0065] In step 730, one or more of the systems described herein may classify the detected cells or a subset of the detected cells into multiple cell types. For example, microscope 100 may classify the detected cells into cell types.

[0066] In some examples, classifying the detected cells may further include assigning a cell lineage to the cell type linking cells of different cell types, and presenting the cell data further includes presenting the cell lineage. The cell lineage may include lineage of cells from one or more of the myeloid, lymphoid, or erythroid lineages. Figures 2, 3, and 4 illustrate exemplary lineages. In some examples, the number of cells classified may be at least 500 or more cells, 1,000 or more cells, 2,000 or more cells, 5,000 or more cells, or 10,000 or more cells.

[0067] In some embodiments, the microscope 100 may classify cells using a machine learning classifier. The machine learning classifier may include one or more models, such as neural networks, convolutional neural networks, decision trees, support vector machines, regression analysis, Bayesian networks, and / or training models. The machine learning classifier may be configured to identify at least ten cell types, such as blasts, immature eosinophils, eosinophils, immature basophils, basophils, promyelocytes, myelocytes, metamyelocytes, bands, neutrophils, monoblasts, monocytes, macrophages, megakaryoblasts, megakaryocytes, erythroblasts, megaloblasts, normoblasts, lymphocytes, and plasma cells.

[0068] In step 740, one or more of the systems described herein may store the cellular data of the classified cells in a memory. For example, microscope 100 may store the cellular data in memory 108. Storing the cellular data may allow the cellular data to be modified and retrieved for presentation to a user.

[0069] In some examples, storing the cellular data may further include selecting at least relevant cells from the detected cells to store in the cellular data. The relevant cells may be selected based on a representative distribution of cell types from the detected cells. The number of relevant cells may be between 100 and 500 cells, between 500 and 1,000 cells, between 1,000 and 2,000 cells, or more than 2,000 cells.

[0070] In step 750, one or more of the systems described herein may present the cellular data. For example, microscope 100 may present the cellular data using user interface 112.

[0071] While the cellular data may be presented, for example, as a table and / or a text readout, the systems and methods described herein advantageously present the cellular data graphically. For example, the microscope 100 may display a cell density heat map (see, e.g., FIG. 6). Figures 2, 3, 4, and 5 show other examples of graphically presenting cellular data.

[0072] In some embodiments, the microscope 100 may receive user input to modify the cell data. The user input may include marking a cell, adding a cell, removing a cell, reclassifying a cell, and / or adding a notification to a cell. The notification may indicate toxicity or an abnormality.

[0073] In some embodiments, the illumination assembly 110 of the microscope 100 may be configured to illuminate the BMA sample at various angles such that the image capture device 102 may be configured to collect multiple images from the BMA sample illuminated by the illumination assembly 110 at various angles. The multiple images may comprise a first minimum resolvable distance for a cellular feature.

[0074] The microscope 100 may generate a computationally reconstructed image from the multiple images (e.g., via a controller 106 operatively coupled to the illumination assembly 110 and the image capture device 102). The computationally reconstructed image may comprise a second minimum resolvable distance for the cellular features. The second minimum resolvable distance may be less than the first minimum resolvable distance.

[0075] In some embodiments, microscope 100 may be configured to identify intact cells and determine an area for further analysis based on the location of the intact cells. For example, microscope 100 may identify intact cells from a preview image such that the area for further analysis can be a recommended scan area. Alternatively, microscope 100 may identify intact cells from a scan area such that an area for further analysis can be suggested for another scan, for analysis by a user, etc.

[0076] 8 illustrates a flowchart of an exemplary method 800 for presenting a bone marrow aspirate analysis. In one example, each of the steps shown in FIG. 8 may represent an algorithm whose structure includes and / or is represented by multiple substeps, an example of which will be provided in more detail below. The steps shown in FIG. 8 may be performed by one or more of the systems and / or portions therein described herein, such as microscope 100 and / or portions therein (e.g., controller 106).

[0077] 8, in step 810, one or more of the systems described herein may receive scanned data of a BMA sample. For example, microscope 100 may receive scanned data of a BMA sample. Microscope 100 may receive scanned data from another scanning device, which may be scanning the BMA sample itself, as described above, or may receive data stored within a computing device.

[0078] In step 820, one or more of the systems described herein may detect particles in the BMA sample from the scanned data. For example, microscope 100 may detect particles in the BMA sample. In some embodiments, microscope 100 may use computer vision or other AI-based image recognition to detect particles, as described herein.

[0079] In step 830, one or more of the systems described herein may detect cells in the BMA sample separate from the detected particles. For example, microscope 100 may detect cells in the BMA sample separate from the detected particles. Microscope 100 may distinguish between particles and cells, for example, using computer vision or other AI-based image recognition.

[0080] In some embodiments, microscope 100 may detect intact cells. In some embodiments, microscope 100 may detect cell density. In some embodiments, microscope 100 may classify the cell type of the detected cells, as further described above.

[0081] In step 840, one or more of the systems described herein may determine a region of interest (ROI) based on the detected cells. For example, microscope 100 may determine a ROI based on the detected cells.

[0082] In some embodiments, the ROI may be further determined based on the detected intact cells. In some embodiments, the ROI may be further determined based on cell density. For example, the ROI may be further determined based on the distance between the detected cells and the detected particles. A desirable ROI may include intact cells, cells dispersed at a relevant density (e.g., not too densely or too sparsely), and cells within a desired range from the particles.

[0083] In some examples, the ROI may be further determined based on associated localization information corresponding to one or more specific locations of the BMA sample. For example, the associated localization information may correspond to a localized disease with characteristics at one or more specific locations. The localized disease may include lymphoma, plasma cell myeloma, mastocytosis, metastatic cancer, storage histiocytosis, crystal storage histiocytosis, and / or granuloma.

[0084] In step 850, one or more of the systems described herein may select a subset of detected cells for the cellular data based, in part, on the ROI and the detected cells. For example, microscope 100 may select a subset of detected cells for the cellular data based, in part, on the ROI and the detected cells.

[0085] In some examples, the ROI may comprise multiple ROIs, each ROI may include a distribution of cells, and a subset of cells may be selected from the subset of the multiple ROIs based on a comparison of the aggregate distribution of cells from the multiple ROIs to the total distribution of cells from the ROIs.

[0086] In some examples, the subset of cells may be further selected based on having a cellular distribution of the cell type that is representative of the total cellular distribution of the cell type in the scan data. The subset of cells may be further selected based on prioritizing one or more cell types for inclusion in the cellular data. For example, the prioritized cell types may include blast cells and / or plasma cells.

[0087] In some embodiments, one or both of the following approaches are used to select a subset of selected cells as cellular data for presentation.

[0088] 1. A subset of ROIs is selected, and cells present within the subset of ROIs are selected. These ROIs can be selected in response to an aggregate set of cells within these ROIs that represents the distribution of cells in the sample. In some embodiments, this also includes evaluation to ensure that specific cell types are clearly represented within the selected subset of ROIs.

[0089] 2. In some embodiments, a subset of cells from the general cell population of a sample is selected to represent the statistics of cells from the general population. For example, a subset of cells can be selected to maintain a percentage corresponding to the general population, and these selected cells may not include all cells from the ROI that contributed cells to the subset. Alternatively, or in combination, a subset of cells may be selected to prioritize one or more focal diseases or to prioritize certain cell types as described herein. In some embodiments, one or more focal diseases correspond to many cells of a specific type or distribution within a specific area of ​​the sample.

[0090] Research related to the present disclosure suggests that it may be useful for users to confirm that a subset of cells is generated without substantial bias. In some embodiments, the processor is configured with instructions for generating a second subset of cells and providing the user with data that may be useful in detecting bias. In some embodiments, the second subset of identified cells comprises randomly selected cells of one or more cell types that were not selected for the identified subset of cells. Data from the second subset can be presented to the user to allow the user to detect bias in the subset.

[0091] In some embodiments, the potential for bias (if any) can be reduced by presenting a random assortment of selected cells for a second subset of cells that were not selected as part of that subset. For example, if an AI process as described herein does not systematically select what a user is interested in, e.g., a cell type, a random selection process will allow the user to become aware of this bias in the selection process when presented with data from the second subset of cells.

[0092] In step 860, one or more of the systems described herein may store the cellular data in a memory. For example, microscope 100 may store the cellular data in memory 108. The cellular data may be stored for modification and / or retrieval for presentation.

[0093] In step 870, one or more of the systems described herein may present the cellular data. For example, microscope 100 may present the cellular data using user interface 112.

[0094] In some examples, presenting the cellular data may include presenting a comparison of cell types between a first set of cells and a second set of cells. In some examples, the comparison may include a ratio between the first set of cells and the second set of cells. The first set of cells may correspond to a selected subset of cells. The second set of cells may correspond to a general population of cells from the BMA sample. For example, FIG. 4 illustrates the comparison as a percentage of cell types among all nucleated cells.

[0095] In some embodiments, the microscope 100 may display a cell density heat map (see, e.g., FIG. 6). In some embodiments, the microscope 100 may display other graphical representations (see, e.g., FIGS. 2, 3, and 5).

[0096] In some embodiments, the microscope 100 may compile the cell data based on user input, for example, user input as described above.

[0097] 9 illustrates a flowchart of an exemplary method 900 for presenting a bone marrow aspirate analysis. In one example, each of the steps shown in FIG. 9 may represent an algorithm whose structure includes and / or is represented by multiple substeps, an example of which will be provided in more detail below. The steps shown in FIG. 9 may be performed by one or more of the systems and / or portions therein described herein, such as microscope 100 and / or portions therein (e.g., controller 106).

[0098] 9, in step 910, one or more of the systems described herein may receive scan data of a BMA sample. For example, microscope 100 may receive scan data of a BMA sample. In some embodiments, microscope 100 may receive scan data as described above.

[0099] In step 920, one or more of the systems described herein may detect cells in the BMA sample. For example, microscope 100 may detect cells in the BMA sample. In some embodiments, microscope 100 may detect cells using computer vision or other AI-based image recognition, as described above.

[0100] In step 930, one or more of the systems described herein may classify the detected cells into one or more cell types. For example, microscope 100 may classify the detected cells into cell types. Microscope 100 may classify the cells using a machine learning classifier running on a processor, as described herein. In some examples, microscope 100 may classify the cells into cell lineages, as described herein.

[0101] In step 940, one or more of the systems described herein may determine, for each one or more cell types, the percentage of the cell type relative to the set of detected cells for the cellular data. For example, a processor of microscope 100 may determine the percentage of each cell type relative to the set of detected cells.

[0102] In some examples, the set of detected cells corresponds to a general population of detected cells or a selected subset of detected cells. Figure 4 illustrates percentages relative to the general population. Alternatively, or in combination, the set of detected cells may comprise a series of cells, and the cellular data may comprise proportions of cell types within the series relative to other cells in the series, such as the percentage of each cell type within the series. In some embodiments, the cellular data may comprise relative proportions of cells that are useful to a system user for making a diagnosis, such as the best cells to present to the user for making a diagnosis.

[0103] In some embodiments, the microscope 100 may perform further analysis to store additional cellular data, as described herein. For example, the cellular data may include a percentage of abnormal cells for one or more cell types (see, e.g., FIG. 4). The cellular data may include a statistical analysis for one or more cell types. For example, the statistical analysis may include at least one of a distribution, a mean, or a median (see, e.g., FIG. 4). In some embodiments, the statistical analysis may include one or more of a distribution, a mean, or a median relative to a reference value, such as a range of reference values ​​for each cell type.

[0104] In step 950, one or more of the systems described herein may store the cell data in a memory. For example, microscope 100 may store the cell data in memory 108.

[0105] In step 960, one or more of the systems described herein may present the cellular data based on the lineage of one or more cell types. For example, microscope 100 may present the cellular data based on the cell lineage using user interface 112.

[0106] In some examples, presenting the cellular data may include a graphical presentation of the lineage of one or more cell types (see, e.g., FIG. 4). The lineage may comprise the lineage of cells from the myeloid, lymphoid, and / or erythroid lineages.

[0107] In some examples, the graphical presentation may include, for each of one or more cell types, a visual comparison of the percentage of the cell type relative to the set of detected cells or to a subset of the set of detected cells (see, e.g., FIG. 4). In some examples, the microscope 100 may receive user input to select a subset of the detected cells. For example, the microscope 100 may receive user input to compare a cell type to cells in a particular area, a general population, etc.

[0108] In some examples, the visual comparison may include, for each of one or more cell types, a shape that is scaled based on the percentage of the cell type (see, e.g., Figures 2, 3, and 4). In some examples, the graphic presentation may include, for each of one or more cell types, a visual comparison based on a range of values ​​(see, e.g., Figure 4). In some examples, the graphic presentation may include one or more colors that correspond to one or more cellular properties. In some examples, presenting the cellular data includes presenting a cell density heat map (see, e.g., Figure 6).

[0109] 10 illustrates a flowchart of an exemplary method 1000 for presenting a bone marrow aspirate analysis. In one example, each of the steps shown in FIG. 10 may represent an algorithm whose structure includes and / or is represented by multiple substeps, an example of which will be provided in more detail below. The steps shown in FIG. 10 may be performed by one or more of the systems and / or portions therein described herein, such as microscope 100 and / or portions therein (e.g., controller 106).

[0110] 10 , in step 1010, one or more of the systems described herein may receive scan data from a BMA sample. For example, microscope 100 may receive scan data from a BMA sample. Microscope 100 may receive scan data from image capture device 102 or another device, as described above.

[0111] In step 1020, one or more of the systems described herein may detect one or more particles or cell locations from the scan data. For example, microscope 100 may detect particles and / or cell locations from the scan data. Microscope 100 may detect particles and / or cell locations using computer vision or other AI-based image recognition, as described above.

[0112] In step 1030, one or more of the systems described herein may determine an analysis area based on one or more of the detected particle or cell locations. For example, microscope 100 may determine the analysis area based on the detected particle and / or cell locations. In some examples, determining the analysis area may further include determining associated locations based on identifying intact cells. For example, microscope 100 may use computer vision or other AI-based image recognition to detect intact cells and determine the analysis area accordingly. In some examples, determining the analysis area may be similar to determining an ROI as described above.

[0113] In step 1040, one or more of the systems described herein may identify cells of one or more cell types from the scanned data. For example, microscope 100 may identify cells of one or more cell types from the scanned data.

[0114] In step 1050, one or more of the systems described herein may analyze the identified cells using statistical analysis. For example, microscope 100 may analyze the identified cells using statistical analysis.

[0115] In some examples, the statistical analysis may be based on the location of the identified cells, hi some examples, the statistical analysis may be based on comparing cell properties to the general population of the identified cells.

[0116] In some examples, the statistical analysis may be adjusted based on user input, which may include adjusting the recommended scan area to include or exclude a user-defined area.

[0117] In step 1060, one or more of the systems described herein may select a subset of the identified cells based on statistical analysis. For example, microscope 100 may select a subset of the identified cells based on statistical analysis. By selecting a subset of cells from the analysis area, a good selection of representative cells can be selected without having to analyze a large portion of the general population of cells. For example, instead of having to analyze thousands of cells, hundreds of cells can be analyzed while achieving the sensitivity required to analyze the sample.

[0118] 11 illustrates a flowchart of an exemplary method 1100 for a user to review a bone marrow aspirate analysis. In one example, each of the steps shown in FIG. 11 may represent an algorithm whose structure includes and / or is represented by multiple sub-steps, examples of which will be provided in more detail below.

[0119] As illustrated in FIG. 11 , in step 1110, one or more of the systems described herein may scan the slide. In step 1120, one or more of the systems described herein may review sample adequacy. In step 1130, one or more of the systems described herein may review megakaryocytes. In step 1140, one or more of the systems described herein may check NDC area, such as particles, cell area, etc. In step 1150, one or more of the systems described herein may review cell and / or area recommendations. In step 1160, one or more of the systems described herein may review pre-classified cells. In step 1170, one or more of the systems described herein may review values, such as cell percentage and / or number, abnormality, size, series, etc. Values ​​may be derived, for example, from statistical analysis as described herein. In step 1180, one or more of the systems described herein may fill out a report. For example, a user may review the cellular data (e.g., as a result of a computer analysis as described herein), make adjustments, descriptions, etc., and fill out a report. In step 1190, one or more of the systems described herein may approve the report. For example, a user may approve and finalize the report. The user may have the necessary permissions (e.g., certification, experience, etc.) to approve the report. In some examples, the user who approves the report may be different from the user who fills out the report, and thus, the user who approves the report may verify the accuracy of the report before finalizing it.

[0120] 12 illustrates a flowchart of an exemplary method 1200 for AI-based bone marrow aspirate analysis. In one example, each of the steps shown in FIG. 12 may represent an algorithm whose structure includes and / or is represented by multiple substeps, an example of which will be provided in more detail below. The steps shown in FIG. 12 may be performed by one or more of the systems and / or portions thereof described herein, such as microscope 100 and / or portions thereof (e.g., controller 106).

[0121] As illustrated in FIG. 12 , in step 1210, one or more of the systems described herein may detect particles on a preview image of the slide and recommend a scan area of ​​the slide. In step 1220, one or more of the systems described herein may scan the slide. In step 1230, one or more of the systems described herein may detect particles from the scan. In step 1240, one or more of the systems described herein may find megakaryocytes and find an area for analysis. For example, the area may be based on intact cells at relevant locations within the slide. In step 1250, one or more of the systems described herein may analyze all relevant cells, such as cells within the area for analysis. In step 1260, one or more of the systems described herein may recommend cells for analysis. The recommendation may be based, for example, on area and / or cell-based statistical analysis. In step 1270, one or more of the systems described herein may adjust the analysis based on a user request, if requested by the user. For example, the user may select a different area and / or cells, request a different type of analysis, request a rescan or scan of a different area, etc.

[0122] 13 illustrates a flowchart of an exemplary method 1300 for presenting a bone marrow aspirate analysis. In one example, each of the steps shown in FIG. 13 may represent an algorithm whose structure includes and / or is represented by multiple substeps, an example of which will be provided in more detail below. The steps shown in FIG. 13 may be performed by one or more of the systems and / or portions therein described herein, such as microscope 100 and / or portions therein (e.g., controller 106).

[0123] 13, in step 1302, one or more of the systems described herein may load a slide into the device. For example, a bone marrow aspirate sample may be mounted on a slide that is loaded into the microscope 100. In step 1304, one or more of the systems described herein may take a preview image of the sample and propose a scan area (e.g., by locating particle locations). As illustrated in FIG. 13, in step 1306, one or more of the systems described herein may scan the sample, e.g., scan the proposed scan area.

[0124] In step 1308, one or more of the systems described herein may locate megakaryocytes in the sample. For example, microscope 100 may allow a user to review, approve, modify, add, or remove detected megakaryocytes from the analysis, estimate their number, etc. Microscope 100 may locate potential areas for analysis (e.g., NDC areas with samples of appropriate cell density and quality). The user may select an area from the suggested ones and / or add a new area. Microscope 100 may perform cell localization and / or cell segmentation on the detected / selected areas. This information may assist microscope 100 in selecting an area and / or assist the user in determining relevant areas. Microscope 100 may recommend relevant cells for analysis based on statistics from the AI ​​analysis. Microscope 100 may display a cell density heat map (e.g., see FIG. 6), particles, analysis areas, and other results on the scan (e.g., see FIG. 5).

[0125] In step 1310, one or more of the systems described herein may view cells from the NDC area via a scanned image or other organization (e.g., thumbnail). The view may include percentage / count estimates. The microscope 100 may allow the user to delete, reclassify, or add cells. The cells may be organized by class (see, e.g., FIGS. 2, 3, and 4). The microscope 100 may allow cells to be viewed as a whole or by NDC area.

[0126] In step 1312, one or more of the systems described herein may allow the user to mark clinical indications (percentages, normal / abnormal, comments, etc.) from the view. In step 1314, one or more of the systems described herein may show an indication (e.g., M:E ratio) in terms of NDC area and / or by area. For example, microscope 100 may present the results in a visual sequence (see, e.g., FIGS. 2, 3, and 4).

[0127] In step 1316, one or more of the systems described herein may allow the user to repeat this process for multiple slides. In step 1318, one or more of the systems described herein may allow the user to analyze iron-stained (e.g., Prussian blue) slides. For example, microscope 100 may allow manual estimation of iron deposits and / or present an estimate that the user can change and / or accept.

[0128] In step 1320, one or more of the systems described herein may aggregate the results into a report format. For example, the microscope 100 may auto-populate or allow the user to fill in fields. The fields may correspond to clinical indications such as those required by ICSH. The microscope 100 may include quantified results from the analysis in the report view and may auto-populate only values ​​that fit certain criteria (e.g., only normal values ​​or only values ​​that are consistent across slides or NDC areas).

[0129] In step 1322, one or more of the systems described herein may track the change history and allow the user to view it.

[0130] Although various methods are described above with reference to separate flowcharts, in some embodiments, one or more steps from the various methods may be combined, performed in a different order, and / or repeated. Additionally, in some embodiments, different users (e.g., users with different permissions, different expertise, etc.) may perform different steps of the methods described herein.

[0131] 14 is a block diagram of an exemplary computing system 1410 capable of implementing one or more of the embodiments described and / or illustrated herein. For example, all or a portion of the computing system 1410 may perform and / or be a means for performing one or more of the steps described herein (such as one or more of the steps illustrated in FIGS. 7, 8, 9, 10, 11, 12, and 13), either alone or in combination with other elements. All or a portion of the computing system 1410 may also perform and / or be a means for performing any other step, method, or process described and / or illustrated herein. All or a portion of the computing system 1410 may correspond to or otherwise be integrated with the microscope 100 (e.g., one or more of the controller 106, the memory 108, and / or the user interface 112).

[0132] Computing system 1410 broadly represents any single- or multi-processor computing device or system capable of executing computer-readable instructions. Examples of computing system 1410 include, but are not limited to, a workstation, a laptop, a client terminal, a server, a distributed computing system, a handheld device, or any other computing system or device. In its most basic configuration, computing system 1410 may include at least one processor 1414 and system memory 1416.

[0133] Processor 1414 generally represents any type or form of physical processing unit (e.g., a hardware-implemented central processing unit) capable of processing data or interpreting and executing instructions. In an embodiment, processor 1414 may receive instructions from a software application or module. These instructions may cause processor 1414 to perform the functions of one or more of the exemplary embodiments described and / or illustrated herein.

[0134] System memory 1416 generally represents any type or form of volatile or non-volatile storage device or medium capable of storing data and / or other computer-readable instructions. Examples of system memory 1416 include, but are not limited to, random access memory (RAM), read-only memory (ROM), flash memory, or any other suitable memory device. Although not required, in some embodiments, computing system 1410 may include both a volatile memory unit (e.g., system memory 1416, etc.) and a non-volatile storage device (e.g., primary storage device 1432, etc., as described in detail below). In one example, one or more of the steps from Figures 7, 8, 9, 10, 11, 12, and 13 may be computer instructions that may be loaded into system memory 1416.

[0135] In some embodiments, system memory 1416 may store and / or load operating system 1440 for execution by processor 1414. In one embodiment, operating system 1440 may include and / or represent software that manages computer hardware and software resources and / or provides common services to computer programs and / or applications on computing system 1410. Examples of operating systems 1440 include, but are not limited to, LINUX®, JUNOS, MICROSOFT WINDOWS®, WINDOWS MOBILE®, MAC OS, APPLE'S IOS, UNIX®, GOOGLE CHROME OS, GOOGLE ANDROID®, SOLARIS®, variations of one or more of the same, and / or any other suitable operating system.

[0136] In an embodiment, the exemplary computing system 1410 may also include one or more components or elements in addition to the processor 1414 and the system memory 1416. For example, as illustrated in FIG. 14, the computing system 1410 may include a memory controller 1418, an input / output (I / O) controller 1420, and a communication interface 1422, each of which may be interconnected via a communication infrastructure 1412. The communication infrastructure 1412 generally represents any type or form of infrastructure capable of facilitating communication between one or more components of a computing device. Examples of the communication infrastructure 1412 include, but are not limited to, communication buses (such as an Industry Standard Architecture (ISA), Peripheral Component Interconnect (PCI), PCI Express (PCIe), or similar buses) and networks.

[0137] Memory controller 1418 generally represents any type or form of device capable of handling memory or data or controlling communications between one or more components of computing system 1410. For example, in one embodiment, memory controller 1418 may control communications between processor 1414, system memory 1416, and I / O controller 1420 via communications infrastructure 1412.

[0138] I / O controller 1420 generally represents any type or form of module capable of coordinating and / or controlling the input and output functions of a computing device. For example, in one embodiment, I / O controller 1420 may control or facilitate the transfer of data between one or more elements of computing system 1410, such as processor 1414, system memory 1416, communications interface 1422, display adapter 1426, input interface 1430, and storage interface 1434.

[0139] 14, computing system 1410 may also include at least one display device 1424 (which may correspond to user interface 112) coupled to I / O controller 1420 via display adapter 1426. Display device 1424 generally represents any type or form of device capable of visually displaying information transferred by display adapter 1426. Similarly, display adapter 1426 generally represents any type or form of device configured to transfer graphics, text, and other data from communications infrastructure 1412 (or from a frame buffer as known in the art) for display on display device 1424.

[0140] 14, the exemplary computing system 1410 may also include at least one input device 1428 (which may correspond to the user interface 112) coupled to the I / O controller 1420 via an input interface 1430. The input device 1428 generally represents any type or form of input device capable of providing either computer- or human-generated input to the exemplary computing system 1410. Examples of the input device 1428 include, but are not limited to, a keyboard, a pointing device, a voice recognition device, variations or combinations of one or more thereof, and / or any other input device.

[0141] Additionally or alternatively, exemplary computing system 1410 may include additional I / O devices. For example, exemplary computing system 1410 may include I / O devices 1436. In this example, I / O devices 1436 may include and / or represent a user interface that facilitates human interaction with computing system 1410. Examples of I / O devices 1436 include, but are not limited to, a computer mouse, a keyboard, a monitor, a printer, a modem, a camera, a scanning device, a microphone, a touchscreen device, variations or combinations of one or more thereof, and / or any other I / O device.

[0142] Communications interface 1422 broadly represents any type or form of communications device or adapter capable of facilitating communications between exemplary computing system 1410 and one or more additional devices. For example, in one embodiment, communications interface 1422 may facilitate communications between computing system 1410 and a private or public network including the additional computing systems. Examples of communications interface 1422 include, but are not limited to, a wired network interface (such as a network interface card), a wireless network interface (such as a wireless network interface card), a modem, and any other suitable interface. In at least one embodiment, communications interface 1422 may provide a direct connection to a remote server via a direct link to a network such as the Internet. Communications interface 1422 may also provide such a connection indirectly, for example, through a local area network (such as an Ethernet network), a personal area network, a telephone or cable network, a cellular telephone connection, a satellite data connection, or any other suitable connection.

[0143] In some embodiments, communications interface 1422 may also represent a host adapter configured to facilitate communication between computing system 1410 and one or more additional network or storage devices via an external bus or communications channel. Examples of host adapters include, but are not limited to, small computer system interface (SCSI) host adapters, universal serial bus (USB) host adapters, Institute of Electrical and Electronics Engineers (IEEE) 1394 host adapters, advanced technology attachment (ATA), parallel ATA (PATA), serial ATA (SATA), and external SATA (eSATA) host adapters, Fibre Channel interface adapters, Ethernet adapters, or the like. Communications interface 1422 may also enable computing system 1410 to engage in distributed or remote computing. For example, communications interface 1422 may receive instructions from a remote device or send instructions to a remote device for execution.

[0144] In some embodiments, system memory 1416 may store and / or load a network communications program 1438 for execution by processor 1414. In one embodiment, network communications program 1438 may include and / or represent software that enables computing system 1410 to establish a network connection 1442 with another computing system (not shown in FIG. 14 ) and / or communicate with other computing systems using communication interface 1422. In this embodiment, network communications program 1438 may direct the flow of downstream traffic sent to other computing systems via network connection 1442. Additionally or alternatively, network communications program 1438 may direct the processing of upstream traffic received from other computing systems via network connection 1442 associated with processor 1414.

[0145] 14, the network communications program 1438 may alternatively be stored and / or loaded within the communications interface 1422. For example, the network communications program 1438 may include and / or represent at least a portion of the software and / or firmware executed by a processor and / or application specific integrated circuit (ASIC) embedded within the communications interface 1422.

[0146] 14 , exemplary computing system 1410 may also include a primary storage device 1432 and a backup storage device 1433 coupled to communications infrastructure 1412 via a storage interface 1434. Storage devices 1432 and 1433 generally represent any type or form of storage device or medium capable of storing data and / or other computer-readable instructions. For example, storage devices 1432 and 1433 may be magnetic disk drives (e.g., so-called hard drives), solid-state drives, floppy disk drives, magnetic tape drives, optical disk drives, flash drives, or the like. Storage interface 1434 generally represents any type or form of interface or device for transferring data between storage devices 1432 and 1433 and other components of computing system 1410. In one example, scan data 1435 (which may correspond to the scan data described herein) and / or cellular data 1437 (which may correspond to the cellular data described herein) may be stored and / or loaded into primary storage device 1432.

[0147] In one embodiment, storage devices 1432 and 1433 may be configured to read from and / or write to removable storage units configured to store computer software, data, or other computer-readable information. Examples of suitable removable storage units include, but are not limited to, floppy disks, magnetic tapes, optical disks, flash memory devices, or the like. Storage devices 1432 and 1433 may also include other similar structures or devices for allowing computer software, data, or other computer-readable instructions to be loaded into computing system 1410. For example, storage devices 1432 and 1433 may be configured to read and write software, data, or other computer-readable information. Storage devices 1432 and 1433 may also be part of computing system 1410 or may be separate devices accessed through other interface systems.

[0148] Many other devices or subsystems may be connected to computing system 1410. Conversely, not all of the components and devices illustrated in FIG. 14 need be present to practice the embodiments described and / or illustrated herein. The above-mentioned devices and subsystems may also be interconnected in ways different from those shown in FIG. 14. Computing system 1410 may also employ any number of software, firmware, and / or hardware configurations. For example, one or more of the exemplary embodiments disclosed herein may be encoded as a computer program (also referred to as computer software, software application, computer-readable instructions, or computer control logic) on a computer-readable medium. The term “computer-readable medium” as used herein generally refers to any form of device, carrier, or medium capable of storing or carrying computer-readable instructions. Examples of computer-readable media include, but are not limited to, transmission-type media such as carrier waves, and non-transitory-type media such as magnetic storage media (e.g., hard disk drives, tape drives, and floppy disks), optical storage media (e.g., compact discs (CDs), digital video discs (DVDs), and BLU-RAY discs), electronic storage media (e.g., solid-state drives and flash media), and other distribution systems.

[0149] A computer-readable medium containing a computer program may be loaded into computing system 1410. All or a portion of the computer program stored on the computer-readable medium may then be stored in system memory 1416 and / or various portions of storage devices 1432 and 1433. When executed by processor 1414, the computer program loaded into computing system 1410 may cause and / or be a means for causing processor 1414 to perform the functions of and / or implement one or more of the exemplary embodiments described and / or illustrated herein. Additionally or alternatively, one or more of the exemplary embodiments described and / or illustrated herein may be implemented in firmware and / or hardware. For example, computing system 1410 may be configured as an application-specific integrated circuit (ASIC) adapted to implement one or more of the exemplary embodiments disclosed herein.

[0150] 15 is a block diagram of an example network architecture 1500 in which client systems 1510, 1520, and 1530 and servers 1540 and 1545 may be coupled to a network 1550. As detailed above, all or a portion of network architecture 1500 may perform and / or be a means for performing one or more of the steps disclosed herein (such as one or more of the steps illustrated in FIGS. 7, 8, 9, 10, 11, 12, and 13), either alone or in combination with other elements. All or a portion of network architecture 1500 may also be used to perform and / or be a means for performing other steps and features described in this disclosure.

[0151] Client systems 1510, 1520, and 1530 generally represent any type or form of computing device or system, such as exemplary computing system 1410 of FIG. 14. Similarly, servers 1540 and 1545 generally represent computing devices or systems, such as application servers or database servers, configured to provide various database services and / or run certain software applications. Network 1550 generally represents any telecommunications or computer network, including, for example, an intranet, a WAN, a LAN, a PAN, or the Internet. In one example, client systems 1510, 1520, and / or 1530 and / or servers 1540 and / or 1545 may include all or a portion of microscope 100 from FIG. 1.

[0152] As illustrated in FIG. 15 , one or more storage devices 1560(1)-(N) may be directly attached to server 1540. Similarly, one or more storage devices 1570(1)-(N) may be directly attached to server 1545. Storage devices 1560(1)-(N) and storage devices 1570(1)-(N) generally represent any type or form of storage device or medium capable of storing data and / or other computer-readable instructions. In an embodiment, storage devices 1560(1)-(N) and storage devices 1570(1)-(N) may represent network-attached storage (NAS) devices configured to communicate with servers 1540 and 1545 using various protocols, such as Network File System (NFS), Server Message Block (SMB), or Common Internet File System (CIFS).

[0153] Servers 1540 and 1545 may also be connected to a storage area network (SAN) fabric 1580. SAN fabric 1580 generally represents any type or form of computer network or architecture capable of facilitating communications between multiple storage devices. SAN fabric 1580 may facilitate communications between servers 1540 and 1545 and multiple storage devices 1590(1)-(N) and / or intelligent storage array 1595. SAN fabric 1580 may also facilitate communications between client systems 1510, 1520, and 1530 and storage devices 1590(1)-(N) and / or intelligent storage array 1595 via network 1550 and servers 1540 and 1545 in a manner that makes devices 1590(1)-(N) and array 1595 appear as locally attached devices to client systems 1510, 1520, and 1530. Like storage devices 1560(1)-(N) and storage devices 1570(1)-(N), storage devices 1590(1)-(N) and intelligent storage array 1595 generally represent any type or form of storage device or medium capable of storing data and / or other computer-readable instructions.

[0154] In one embodiment, with reference to exemplary computing system 1410 of FIG. 14 , a communications interface, such as communications interface 1422 of FIG. 14 , may be used to provide connectivity between each client system 1510, 1520, and 1530 and network 1550. Client systems 1510, 1520, and 1530 may be able to access information on server 1540 or 1545, for example, using a web browser or other client software. Such software may enable client systems 1510, 1520, and 1530 to access data hosted by server 1540, server 1545, storage devices 1560(1)-(N), storage devices 1570(1)-(N), storage devices 1590(1)-(N), or intelligent storage array 1595. While FIG. 15 depicts the use of a network (such as the Internet) to exchange data, the embodiments described and / or illustrated herein are not limited to the Internet or any particular network-based environment.

[0155] In at least one embodiment, all or a portion of one or more of the exemplary embodiments disclosed herein may be encoded as a computer program and loaded onto and executed by server 1540, server 1545, storage devices 1560(1)-(N), storage devices 1570(1)-(N), storage devices 1590(1)-(N), intelligent storage array 1595, or any combination thereof. All or a portion of one or more of the exemplary embodiments disclosed herein may also be encoded as a computer program and stored within server 1540, executed by server 1545, and distributed to client systems 1510, 1520, and 1530 via network 1550.

[0156] As detailed above, one or more components of computing system 1410 and / or network architecture 1500, either alone or in combination with other elements, may perform and / or be a means for performing one or more steps of an exemplary method for bone marrow aspirate analysis.

[0157] As described herein, the computing devices and systems described and / or illustrated herein broadly represent any type or form of computing device or system capable of executing computer-readable instructions such as those contained in the modules described herein. In their most basic configurations, these computing devices may each include at least one memory device and at least one physical processor.

[0158] The terms "memory" or "memory device" as used herein generally refer to any type or form of volatile or non-volatile storage device or medium capable of storing data and / or computer-readable instructions. In one example, a memory device may store, load, and / or maintain one or more of the modules described herein. Examples of memory devices include, but are not limited to, random access memory (RAM), read-only memory (ROM), flash memory, hard disk drives (HDDs), solid-state drives (SSDs), optical disk drives, caches, variations or combinations of one or more thereof, or any other suitable storage memory.

[0159] Additionally, the terms “processor” or “physical processor” as used herein generally refer to any type or form of hardware-implemented processing unit capable of interpreting and / or executing computer-readable instructions. In one example, a physical processor may access and / or modify one or more modules stored within a memory device described above. Examples of physical processors include, but are not limited to, a microprocessor, a microcontroller, a central processing unit (CPU), a field programmable gate array (FPGA) implementing a soft-core processor, an application-specific integrated circuit (ASIC), portions of one or more thereof, variations or combinations of one or more thereof, or any other suitable physical processor. A processor may comprise, for example, a distributed processor system running parallel processors, or a remote processor such as a server, and combinations thereof.

[0160] Although illustrated as separate elements, the method steps described and / or illustrated herein may represent parts of a single application. Additionally, in some embodiments, one or more of these steps may represent or correspond to one or more software applications or programs that, when executed by a computing device, may cause the computing device to perform one or more tasks, such as method steps.

[0161] Additionally, one or more of the devices described herein may transform data, physical devices, and / or representations of physical devices from one form to another. Additionally or alternatively, one or more of the modules listed herein may transform a processor, volatile memory, non-volatile memory, and / or any other portion of a physical computing device from one form of computing device to another form of computing device by executing on the computing device, storing data on the computing device, and / or otherwise interacting with the computing device.

[0162] The term "computer-readable medium" as used herein generally refers to any form of device, carrier, or medium capable of storing or carrying computer-readable instructions. Examples of computer-readable media include, but are not limited to, transmission-type media such as carrier waves, and non-transitory-type media such as magnetic storage media (e.g., hard disk drives, tape drives, and floppy disks), optical storage media (e.g., compact discs (CDs), digital video discs (DVDs), and BLU-RAY discs), electronic storage media (e.g., solid-state drives and flash media), and other distributed systems.

[0163] Those skilled in the art will recognize that any process or method disclosed herein can be modified in many ways. The process parameters and sequence of steps described and / or illustrated herein are given as examples only and can be varied as desired. For example, although the steps illustrated and / or described herein may be shown or discussed in a particular order, these steps do not necessarily have to be performed in the order shown or discussed.

[0164] The various exemplary methods described and / or illustrated herein may also omit one or more of the steps described or illustrated herein, or may include additional steps in addition to those disclosed. Furthermore, the steps of any method as disclosed herein can be combined with any one or more steps of any other method as disclosed herein.

[0165] A processor as described herein can be configured to perform one or more steps of any of the methods disclosed herein. Alternatively, or in combination, a processor can be configured to combine one or more steps of one or more methods as disclosed herein.

[0166] Unless otherwise stated, the terms "connected to" and "coupled to" (and their derivatives) as used in this specification and claims shall be interpreted as allowing both direct and indirect (i.e., via other elements or components) connections. Additionally, the terms "a" or "an" as used in this specification and claims shall be interpreted as meaning "at least one of." Finally, for ease of use, the terms "including" and "having" (and their derivatives) as used in this specification and claims shall be synonymous with and have the same meaning as the word "comprising."

[0167] A processor as disclosed herein may be configured with instructions to perform any one or more steps of any method as disclosed herein.

[0168] It should be understood that the terms "first," "second," "third," etc. may be used herein to describe various layers, elements, components, regions, or sections without referring to any particular order or sequence of events. These terms are merely used to distinguish one layer, element, component, region, or section from another layer, element, component, region, or section. A first layer, element, component, region, or section as described herein could be referred to as a second layer, element, component, region, or section without departing from the teachings of the present disclosure.

[0169] As used herein, the term "or" is used inclusively to refer to items as alternatives and in combination.

[0170] As used herein, letters such as numbers refer to like elements.

[0171] Appendix 1. A system for scanning a bone marrow aspirate (BMA) sample, comprising: a scanning device for scanning the BMA sample; a processor coupled to the scanning device and a memory and configured to execute instructions to cause the system to obtain scan data of the BMA sample using the scanning device, detect cells in the BMA sample from the scan data, classify the detected cells into a plurality of cell types, and store cellular data of the classified cells in memory; and a display configured to present the cellular data.

[0172] Clause 2. The system of Clause 1, wherein the processor is configured to detect particles in the BMA sample and detect cells in the BMA sample separate from the detected particles.

[0173] Clause 3. The system of Clause 2, wherein the particles comprise one or more clusters of adipose or hematopoietic cells.

[0174] Clause 4. The system of Clause 1, further comprising an image capture device having an effective numerical aperture (NA) of at least 0.8, wherein the detected cells comprise at least 100 cells, and the instructions further comprise instructions for scanning an area of ​​at least 0.5 square cm.

[0175] Appendix 5. The system of Appendix 1, wherein classifying the detected cells further comprises identifying a cell lineage connecting cells of different cell types, and presenting the cell data further comprises presenting the cell lineage.

[0176] Clause 6. The system of Clause 5, wherein the cell lineage comprises a lineage of cells from one or more of the myeloid, lymphoid, or erythroid lineages.

[0177] Appendix 7. The system of Appendix 1, wherein the step of storing the cellular data further comprises the step of selecting, from the detected cells, at least relevant cells to be stored in the cellular data.

[0178] Clause 8. The system of Clause 7, wherein the relevant cells are selected based on a representative distribution of cell types from the detected cells.

[0179] Item 9. The system of Item 7, wherein the number of cells involved is 100-500 cells, 500-1,000 cells, 1,000-2,000 cells, or more than 2,000 cells.

[0180] Appendix 10. The system of Appendix 1, wherein the scan time for acquiring the scan data is faster than 20 minutes per square cm, 15 minutes per square cm, 10 minutes per square cm, 5 minutes per square cm, or 2 minutes per square cm.

[0181] Clause 11. The system of Clause 1, wherein the scanned data comprises data from at least 95% of the entire area of ​​the BMA sample.

[0182] Appendix 12. The system of Appendix 1, wherein the instructions further comprise instructions for analyzing a preview image corresponding to the BMA sample and locating particles, the preview image having a lower resolution than the scan data, and determining a recommended scan area for the BMA sample based on the analysis.

[0183] Clause 13. The system of Clause 12, wherein the recommended scan area is further determined based on user input.

[0184] Clause 14. The system of Clause 13, wherein user input includes adjusting the recommended scan area to include or exclude a user-defined area.

[0185] Clause 15. The system of Clause 12, wherein the instructions for acquiring scan data further comprise instructions for scanning the recommended scan area using a scanning device.

[0186] Clause 16. The system of Clause 1, wherein the instructions further comprise instructions for receiving user input to modify the cellular data.

[0187] Clause 17. The system of Clause 16, wherein the user input includes at least one of marking a cell, adding a cell, deleting a cell, reclassifying a cell, or adding a notification to a cell.

[0188] Clause 18. The system of Clause 17, wherein the notification indicates at least one of toxicity or abnormality.

[0189] Clause 19. The system of Clause 1, wherein the scanned data includes high-resolution image data of the BMA sample.

[0190] Clause 20. The system of clause 19, wherein the high-resolution image data includes image data of the BMA sample scanned using an effective numerical aperture (NA) of at least 0.8.

[0191] Clause 21. The system of Clause 19, wherein the high-resolution image data includes image data of the BMA sample scanned using an effective numerical aperture (NA) of at least 0.9.

[0192] Clause 22. The system of Clause 19, wherein the high-resolution image data includes image data of the BMA sample scanned using an effective numerical aperture (NA) of at least 1.0.

[0193] Clause 23. The system of Clause 1, wherein the scanned data includes at least 0.5 square cm of the BMA sample, at least 1 square cm of the BMA sample, at least 1.5 square cm of the BMA sample, or at least 2 square cm of the BMA sample.

[0194] Clause 24. The system of Clause 1, wherein the number of cells detected is at least one of 500 or more cells, 1,000 or more cells, 2,000 or more cells, 5,000 or more cells, or 10,000 or more cells.

[0195] Clause 25. The system of Clause 1, wherein the number of cells classified is at least one of 500 or more cells, 1,000 or more cells, 2,000 or more cells, 5,000 or more cells, or 10,000 or more cells.

[0196] Appendix 26. The system of Appendix 1, wherein the processor is configured to classify the cells using a machine learning classifier, the machine learning classifier comprising one or more models selected from the group consisting of neural networks, convolutional neural networks, decision trees, support vector machines, regression analysis, Bayesian networks, and training models.

[0197] Clause 27. The system of Clause 26, wherein the machine learning classifier is configured to identify a plurality of at least ten cell types selected from the group consisting of blasts, immature eosinophils, eosinophils, immature basophils, basophils, promyelocytes, myelocytes, metamyelocytes, bands, neutrophils, monoblasts, monocytes, macrophages, megakaryoblasts, megakaryocytes, erythroblasts, megaloblasts, normoblasts, lymphocytes, and plasma cells.

[0198] Appendix 28. The system of Appendix 1, wherein the scanning apparatus comprises an illumination assembly configured to illuminate the BMA sample at a plurality of angles; and an image capture device configured to collect a plurality of images from the BMA sample illuminated by the illumination assembly at a plurality of angles, the plurality of images comprising a first minimum resolvable distance for cellular features; and a processor operatively coupled to the illumination assembly and the image capture device, the processor configured with instructions for generating a computationally reconstructed image from the plurality of images, the computationally reconstructed image comprising a second minimum resolvable distance for cellular features, the second minimum resolvable distance being less than the first minimum resolvable distance.

[0199] Appendix 29. The system of Appendix 1, wherein the processor is configured to display a cell density heat map.

[0200] Addendum 30. The system of Addendum 1, wherein the processor is configured with instructions for identifying intact cells and determining an area for further analysis based on the location of the intact cells.

[0201] Appendix 31. A system for scanning a bone marrow aspirate (BMA) sample, comprising: a processor coupled to a memory and configured to execute instructions to cause the system to receive scan data of the BMA sample, detect particles in the BMA sample from the scan data, detect cells in the BMA sample distinct from the detected particles, determine a region of interest (ROI) based on the detected cells, select a subset of the detected cells for the cellular data based in part on the ROI and the detected cells, and store the cellular data in the memory; and a display configured to present the cellular data.

[0202] Appendix 32. The system of Appendix 31, wherein the step of detecting cells further comprises the step of detecting intact cells, and the ROI is further determined based on the detected intact cells.

[0203] Appendix 33. The system of Appendix 31, wherein the step of detecting cells further comprises the step of detecting cell density, and the ROI is further determined based on the cell density.

[0204] Addendum 34. The system of Addendum 31, wherein the ROI is further determined based on the distance between the detected cell and the detected particle.

[0205] Appendix 35. The system of Appendix 31, wherein the ROI is further determined based on associated localization information corresponding to one or more specific locations of the BMA sample.

[0206] Appendix 36. The system of Appendix 31, wherein the associated localization information corresponds to localized disease with features in one or more specific locations.

[0207] 37. The system of claim 36, wherein the localized disease comprises one or more of lymphoma, plasma cell myeloma, mastocytosis, metastatic carcinoma, storage histiocytosis, crystal storage histiocytosis, or granuloma.

[0208] Clause 38. The system of clause 31, wherein detecting the cell further comprises classifying a cell type of the cell.

[0209] Addendum 39. The system of Addendum 31, wherein the ROI comprises a plurality of ROIs, each of the plurality of ROIs comprising a distribution of cells, and wherein a subset of cells is selected from the subset of the plurality of ROIs based on a comparison of the collective distribution of cells from the plurality of ROIs to a total distribution of cells from the ROI.

[0210] Appendix 40. The system of Appendix 39, wherein the subset of cells is further selected based on having a cell distribution of the cell type that represents the total cell distribution of the cell type in the scanned data.

[0211] Clause 41. The system of Clause 39, wherein the subset of cells is further selected based on prioritizing one or more cell types for inclusion in the cellular data.

[0212] Clause 42. The system of clause 41, wherein the one or more prioritized cell types include blast cells or plasma cells.

[0213] Appendix 43. The system of Appendix 31, wherein presenting the cell data includes presenting a comparison of cell types between the first set of cells and the second set of cells, and optionally the comparison comprises a ratio.

[0214] Clause 44. The system of clause 43, wherein the first set of cells corresponds to a selected subset of cells.

[0215] 45. The system of claim 43, wherein the second set of cells corresponds to a general population of cells from a BMA sample.

[0216] Addendum 46. The system of Addendum 31, wherein the processor is further configured to edit the cell data based on user input.

[0217] Addendum 47. The system of Addendum 31, wherein the processor is configured to display a cell density heat map.

[0218] Appendix 48. A system for scanning a bone marrow aspirate (BMA) sample, comprising: a processor coupled to a memory and configured to execute instructions that cause the system to receive scan data of the BMA sample, detect cells in the BMA sample, classify the detected cells into one or more cell types, determine, for each one or more cell types, a percentage of the cell type relative to the set of detected cells associated with the cellular data, and store the cellular data in the memory; and a display configured to present the cellular data based on a series of one or more cell types.

[0219] Clause 49. The system of clause 48, wherein the set of detected cells corresponds to a general population of detected cells or a selected subset of detected cells.

[0220] Appendix 50. The system of Appendix 48, wherein the cellular data includes a percentage of abnormal cells for each of one or more cell types.

[0221] Appendix 51. The system of Appendix 48, wherein the cellular data includes statistical analysis by one or more cell types.

[0222] Addendum 52. The system of Addendum 51, wherein the statistical analysis includes at least one of a distribution, a mean, or a median.

[0223] Clause 53. The system of Clause 48, wherein the step of presenting the cellular data includes a graphical presentation of a series of one or more cell types.

[0224] Appendix 54. The system of Appendix 53, wherein the graphical presentation includes, for each of one or more cell types, a visual comparison of the percentage of the cell type relative to the set of detected cells or to a subset of the set of detected cells.

[0225] Clause 55. The system of Clause 54, wherein the processor is configured with instructions for receiving user input to select a subset of the detected cells.

[0226] Clause 56. The system of clause 54, wherein the visual comparison includes, for each of one or more cell types, shapes that are scaled based on the percentage of the cell type.

[0227] Clause 57. The system of clause 53, wherein the graphical presentation includes a visual comparison based on a range of values ​​for each of the one or more cell types.

[0228] Addendum 58. The system of Addendum 53, wherein the graphical presentation includes one or more colors corresponding to one or more cellular properties.

[0229] Addendum 59. The system of Addendum 48, wherein presenting the cell data includes presenting a cell density heat map.

[0230] Clause 60. The system of clause 48, wherein the lineage comprises a lineage of cells from one or more of the myeloid, lymphoid, or erythroid lineages.

[0231] Appendix 61. A system for scanning a bone marrow aspirate (BMA) sample, comprising a processor coupled to a memory and configured to execute instructions to cause the system to receive scan data from the BMA sample, detect one or more particles or cell locations from the scan data, determine an analysis area based on the one or more detected particles or cell locations, identify cells of one or more cell types from the scan data, analyze the identified cells using statistical analysis, and select a subset of the identified cells based on the statistical analysis.

[0232] Item 62. The system of Item 61, wherein determining the analysis area further comprises determining an associated location based on identifying intact cells.

[0233] 63. The system of claim 61, wherein the statistical analysis is based on the location of the identified cells.

[0234] Appendix 64. The system of Appendix 61, wherein the statistical analysis is based on comparing cell properties to a general population of identified cells.

[0235] Clause 65. The system of Clause 61, further comprising adjusting the statistical analysis based on user input.

[0236] Addendum 66. The system of Addendum 65, wherein user input includes adjusting the recommended scan area to include or exclude a user-defined area.

[0237] Item 67. The system of Item 61, wherein the second subset of identified cells comprises randomly selected cells of one or more cell types not selected for the subset of identified cells.

[0238] Clause 68. The system of Clause 67, wherein the processor is configured with instructions to present data from the second subset to a user and detect bias in the subset.

[0239] Appendix 69. A method for scanning a bone marrow aspirate (BMA) sample, comprising the steps of scanning the BMA sample with a scanning device, obtaining scan data of the BMA sample using the scanning device, detecting cells in the BMA sample from the scan data, classifying the detected cells into a plurality of cell types, storing cell data of the classified cells in a memory, and presenting the cell data on a display.

[0240] Clause 70. The method of clause 69, wherein particles in a BMA sample are detected and cells in the BMA sample separate from the detected particles are detected.

[0241] Clause 71. The method of clause 70, wherein the particles comprise one or more clusters of adipose or hematopoietic cells.

[0242] Addendum 72. The method of Addendum 69, further comprising the step of scanning the sample with an image capture device having an effective numerical aperture (NA) of at least 0.8, the detected cells comprising at least 100 cells, and an area of ​​at least 0.5 square cm being scanned.

[0243] Addendum 73. The method of Addendum 69, wherein the step of classifying the detected cells further comprises a step of identifying a cell lineage connecting cells of different cell types, and the step of presenting the cell data further comprises a step of presenting the cell lineage.

[0244] Clause 74. The method of clause 73, wherein the cell lineage comprises a lineage of cells from one or more of the myeloid, lymphoid, or erythroid lineages.

[0245] Clause 75. The method of clause 69, wherein the step of storing the cellular data further comprises the step of selecting, from the detected cells, at least relevant cells to be stored in the cellular data.

[0246] 76. The method of claim 75, wherein relevant cells are selected based on a representative distribution of cell types from the detected cells.

[0247] Item 77. The method of item 75, wherein the number of cells involved is 100-500 cells, 500-1,000 cells, 1,000-2,000 cells, or more than 2,000 cells.

[0248] Addendum 78. The method of Addendum 69, wherein the scanning time for acquiring the scan data is faster than 20 minutes per square cm, 15 minutes per square cm, 10 minutes per square cm, 5 minutes per square cm, or 2 minutes per square cm.

[0249] Clause 79. The method of Clause 69, wherein the scanned data comprises data from at least 95% of the entire area of ​​the BMA sample.

[0250] Addendum 80. The method of Addendum 69, further comprising: analyzing a preview image corresponding to the BMA sample and locating particles, the preview image having a lower resolution than the scan data; and determining a recommended scan area for the BMA sample based on the analysis.

[0251] Clause 81. The method of clause 80, wherein the recommended scan area is further determined based on user input.

[0252] Clause 82. The method of clause 81, wherein user input includes adjusting the recommended scan area to include or exclude a user-defined area.

[0253] Clause 83. The method of Clause 80, wherein the instructions for acquiring scan data further comprise instructions for scanning the recommended scan area using a scanning device.

[0254] Clause 84. The method of Clause 69, wherein the instructions further comprise instructions for receiving user input to modify the cell data.

[0255] Addendum 85. The method of Addendum 84, wherein the user input includes at least one of marking a cell, adding a cell, deleting a cell, reclassifying a cell, or adding a notification to a cell.

[0256] Addendum 86. The method of Addendum 85, wherein the notification indicates at least one of toxicity or abnormality.

[0257] Addendum 87. The method of Addendum 69, wherein the scanned data includes high-resolution image data of the BMA sample.

[0258] 88. The method of claim 87, wherein the high-resolution image data includes image data of the BMA sample scanned using an effective numerical aperture (NA) of at least 0.8.

[0259] 89. The method of claim 87, wherein the high-resolution image data includes image data of the BMA sample scanned using an effective numerical aperture (NA) of at least 0.9.

[0260] Addendum 90. The method of Addendum 87, wherein the high-resolution image data includes image data of the BMA sample scanned using an effective numerical aperture (NA) of at least 1.0.

[0261] Clause 91. The method of clause 69, wherein the scanned data comprises at least 0.5 square cm of the BMA sample, at least 1 square cm of the BMA sample, at least 1.5 square cm of the BMA sample, or at least 2 square cm of the BMA sample.

[0262] Appendix 92. The method of Appendix 69, wherein the number of cells detected is at least one of 500 or more cells, 1,000 or more cells, 2,000 or more cells, 5,000 or more cells, or 10,000 or more cells.

[0263] Appendix 93. The method of Appendix 69, wherein the number of cells sorted is at least one of 500 or more cells, 1,000 or more cells, 2,000 or more cells, 5,000 or more cells, or 10,000 or more cells.

[0264] Addendum 94. The method of Addendum 69, wherein the cells are classified using a machine learning classifier, the machine learning classifier comprising one or more models selected from the group consisting of neural networks, convolutional neural networks, decision trees, support vector machines, regression analysis, Bayesian networks, and training models.

[0265] Clause 95. The method of Clause 94, wherein the machine learning classifier is configured to identify a plurality of at least ten cell types selected from the group consisting of blasts, immature eosinophils, eosinophils, immature basophils, basophils, promyelocytes, myelocytes, metamyelocytes, bands, neutrophils, monoblasts, monocytes, macrophages, megakaryoblasts, megakaryocytes, erythroblasts, megaloblasts, normoblasts, lymphocytes, and plasma cells.

[0266] Addendum 96. The method of Addendum 69, further comprising the steps of: illuminating the BMA sample at a plurality of angles using an illumination assembly; and collecting a plurality of images from the BMA sample illuminated by the illumination assembly at the plurality of angles using an image capture device, the plurality of images comprising a first minimum resolvable distance for cellular features; and wherein a computationally reconstructed image is generated from the plurality of images, the computationally reconstructed image comprising a second minimum resolvable distance for cellular features, the second minimum resolvable distance being less than the first minimum resolvable distance.

[0267] Addendum 97. The method of Addendum 69, further comprising displaying a cell density heat map.

[0268] 98. The method of claim 69, wherein intact cells are identified and an area for further analysis is determined based on the location of the intact cells.

[0269] Addendum 99. A method for scanning a bone marrow aspirate (BMA) sample, the method comprising: receiving scan data of the BMA sample; detecting particles in the BMA sample from the scan data; detecting cells in the BMA sample distinct from the detected particles; determining a region of interest (ROI) based on the detected cells; selecting a subset of the detected cells for the cellular data based, in part, on the ROI and the detected cells; storing the cellular data in a memory; and presenting the cellular data.

[0270] Addendum 100. The method of Addendum 99, wherein the step of detecting cells further comprises the step of detecting intact cells, and the ROI is further determined based on the detected intact cells.

[0271] Addendum 101. The method of Addendum 99, wherein the step of detecting cells further comprises the step of detecting cell density, and the ROI is further determined based on the cell density.

[0272] Addendum 102. The method of Addendum 99, wherein the ROI is further determined based on the distance between the detected cell and the detected particle.

[0273] Addendum 103. The method of Addendum 99, wherein the ROI is further determined based on associated localization information corresponding to one or more specific locations of the BMA sample.

[0274] Appendix 104. The method of Appendix 99, wherein the associated localization information corresponds to localized disease with characteristics in one or more specific locations.

[0275] 105. The method of claim 104, wherein the localized disease comprises one or more of lymphoma, plasma cell myeloma, mastocytosis, metastatic carcinoma, storage histiocytosis, crystal storage histiocytosis, or granuloma.

[0276] Clause 106. The method of clause 99, wherein detecting the cells further comprises classifying the cell type of the cells.

[0277] Addendum 107. The method of Addendum 99, wherein the ROI comprises a plurality of ROIs, each of the plurality of ROIs comprising a distribution of cells, and wherein a subset of cells is selected from the subset of the plurality of ROIs based on a comparison of the collective distribution of cells from the plurality of ROIs with the total distribution of cells from the ROI.

[0278] Addendum 108. The method of Addendum 107, wherein the subset of cells is further selected based on having a cell distribution of the cell type that represents the total cell distribution of the cell type in the scanned data.

[0279] Appendix 109. The method of Appendix 107, wherein the subset of cells is further selected based on prioritizing one or more cell types for inclusion in the cellular data.

[0280] Item 110. The method of item 109, wherein the one or more prioritized cell types include blast cells or plasma cells.

[0281] Clause 111. The method of clause 99, wherein presenting the cell data includes presenting a comparison of cell types between the first set of cells and the second set of cells, and optionally, the comparison comprises a ratio.

[0282] Clause 112. The method of clause 111, wherein the first set of cells corresponds to a selected subset of cells.

[0283] Clause 113. The method of clause 111, wherein the second set of cells corresponds to a general population of cells from the BMA sample.

[0284] Addendum 114. The method of Addendum 99, wherein the cell data is compiled based on user input.

[0285] Appendix 115. The method of appendix 99, wherein a display of a cell density heat map is displayed.

[0286] Appendix 116. A method for scanning a bone marrow aspirate (BMA) sample, comprising receiving scan data of the BMA sample; detecting cells in the BMA sample; classifying the detected cells into one or more cell types; for each of the one or more cell types, determining a percentage of the cell type relative to a set of detected cells associated with the cell data; storing the cell data in a memory; and presenting the cell data based on a series of one or more cell types.

[0287] Item 117. The method of Item 116, wherein the set of detected cells corresponds to a general population of detected cells or a selected subset of detected cells.

[0288] Appendix 118. The method of Appendix 116, wherein the cellular data includes a percentage of abnormal cells for each of one or more cell types.

[0289] Appendix 119. The method of Appendix 116, wherein the cellular data includes statistical analysis by one or more cell types.

[0290] Appendix 120. The method of Appendix 119, wherein the statistical analysis includes at least one of a distribution, a mean, or a median.

[0291] Clause 121. The method of clause 116, wherein the step of presenting the cellular data includes a graphical presentation of a series of one or more cell types.

[0292] Addendum 122. The method of Addendum 121, wherein the graphical presentation includes, for each of one or more cell types, a visual comparison of the percentage of the cell type relative to the set of detected cells or to a subset of the set of detected cells.

[0293] Clause 123. The method of Clause 122, wherein user input is received to select a subset of the detected cells.

[0294] Clause 124. The method of clause 122, wherein the visual comparison includes, for each of one or more cell types, shapes that are scaled based on the percentage of the cell type.

[0295] Appendix 125. The method of Appendix 121, wherein the graphical presentation includes a visual comparison based on a range of values ​​for each of one or more cell types.

[0296] Addendum 126. The method of Addendum 121, wherein the graphical representation includes one or more colors corresponding to one or more cellular properties.

[0297] Addendum 127. The method of Addendum 116, wherein presenting the cell data includes presenting a cell density heat map.

[0298] Clause 128. The method of clause 116, wherein the lineage comprises a lineage of cells from one or more of the myeloid, lymphoid, or erythroid lineages.

[0299] Addendum 129. A method for scanning a bone marrow aspirate (BMA) sample, comprising receiving scan data from the BMA sample; detecting one or more particles or cell locations from the scan data; determining an analysis area based on the one or more detected particles or cell locations; identifying cells of one or more cell types from the scan data; analyzing the identified cells using statistical analysis; and selecting a subset of the identified cells based on the statistical analysis.

[0300] Item 130. The method of item 129, wherein determining the analysis area further comprises determining an associated location based on identifying intact cells.

[0301] 131. The method of claim 129, wherein the statistical analysis is based on the location of the identified cells.

[0302] Item 132. The method of item 129, wherein the statistical analysis is based on comparing cell properties to a general population of identified cells.

[0303] Clause 133. The method of clause 129, further comprising adjusting the statistical analysis based on user input.

[0304] Addendum 134. The method of Addendum 129, wherein user input includes adjusting the recommended scan area to include or exclude a user-defined area.

[0305] Item 135. The method of item 129, wherein the second subset of identified cells comprises randomly selected cells of one or more cell types not selected for the subset of identified cells.

[0306] Clause 136. The method of clause 135, wherein data from a second subset is presented to a user to detect bias in the subset.

[0307] The embodiments of the present disclosure are shown and described herein and are provided by way of example only. Those skilled in the art will recognize numerous adaptations, modifications, variations, and substitutions without departing from the scope of the present disclosure. Several alternatives and combinations of the embodiments disclosed herein may be utilized without departing from the scope of the present disclosure and invention disclosed herein. Accordingly, the scope of the presently disclosed invention is to be defined solely by the scope of the appended claims and their equivalents.

Claims

[Claim 1] The invention described in this specification.

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