WO2019198094A1 - Procédé et système d'estimation du nombre total de cellules sanguines dans un frottis sanguin - Google Patents

Procédé et système d'estimation du nombre total de cellules sanguines dans un frottis sanguin Download PDF

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Publication number
WO2019198094A1
WO2019198094A1 PCT/IN2019/050197 IN2019050197W WO2019198094A1 WO 2019198094 A1 WO2019198094 A1 WO 2019198094A1 IN 2019050197 W IN2019050197 W IN 2019050197W WO 2019198094 A1 WO2019198094 A1 WO 2019198094A1
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Prior art keywords
blood
blood cells
regions
images
uniform
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PCT/IN2019/050197
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English (en)
Inventor
Dheeraj MUNDHRA
Bhuvan MALLADIHALLI SHASHIDHARA
Shreepad POTADAR
Bharath Cheluvaraju
Tathagato RAI DASTIDAR
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Sigtuple Technologies Private Limited
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Publication of WO2019198094A1 publication Critical patent/WO2019198094A1/fr

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/483—Physical analysis of biological material
    • G01N33/487—Physical analysis of biological material of liquid biological material
    • G01N33/49—Blood
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
    • G01N15/06—Investigating concentration of particle suspensions
    • G01N15/0606—Investigating concentration of particle suspensions by collecting particles on a support
    • G01N15/0612—Optical scan of the deposits
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/23—Clustering techniques
    • G06F18/232—Non-hierarchical techniques
    • G06F18/2321—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/60—Type of objects
    • G06V20/69—Microscopic objects, e.g. biological cells or cellular parts
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/60—Type of objects
    • G06V20/69—Microscopic objects, e.g. biological cells or cellular parts
    • G06V20/693—Acquisition
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/60—Type of objects
    • G06V20/69—Microscopic objects, e.g. biological cells or cellular parts
    • G06V20/695—Preprocessing, e.g. image segmentation
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
    • G01N15/01—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials specially adapted for biological cells, e.g. blood cells
    • G01N2015/012—Red blood cells

Definitions

  • TITLE “METHOD AND SYSTEM FOR ESTIMATING TC OF BLOOD CELLS IN A BLOOD SMEAR”
  • the present subject mater relates to study of blood cells/ hematology.
  • the present subject matter relates more particularly, but not exclusively, to a method and a system for estimating Total Count (TC) of blood cells in a blood smear.
  • TC Total Count
  • CBC Complete blood count
  • RBC Red Blood Cells
  • WBC White Blood Cells
  • platelets is a basic screening hematology test used to diagnose individuals with abnormalities and for determining seventy of health conditions.
  • Abnormally high or low counts of blood cells may indicate the presence of many forms of diseases.
  • blood counts are among the most commonly performed blood tests in the field of hematology, as they provide an overview of general health status of a patient.
  • TC of WBCs may be used for diagnosis of autoimmune disorders like lupus, rheumatoid arthritis (decrease in TC of WBCs), leukocytosis (increase in TC of WBCs) and the like.
  • Hematology analyzers are used for determining various parameters from a blood sample.
  • Existing haematology analyzers use techniques such as flow cytometry to estimate TC of blood cells in a blood sample.
  • the existing hematology analyzers measure the type of blood cell by analyzing data about the size of ceils and aspects of light as they pass through the cells. The amount of light scattered by the blood cells are mainly dependent on the size of the blood cells.
  • certain abnormal cells in the blood sample may not be identified correctly and may require manual review of the instrument's results for identifying any abnormal cells which could affect the estimation of TC of blood cells.
  • Manual counting of blood cells is subjected to sampling error because only few cells are counted compared to automated analysis.
  • the analyzers mentioned above use reagents in every analysis, thereby, increasing the cost.
  • pathologists scan the blood smear to determine the monolayer region.
  • the monolayer is determined as the blood cells are well separated and overlap less with each other in the monolayer.
  • random Field of Views FoVs
  • the blood cells are identified manually and the counts of each type of blood cell are determined in the random FoV s.
  • an average number of cells per FoV Total number of cells divided by the number of Fields of View' is multiplied by a coefficient to arrive at an estimation of TC of the blood cells.
  • the co-efficient is an approximated number published in the medical books and using the coefficient may result m an over-estimation or under-estimation of count of blood cells.
  • the coefficient used by a first pathologist may not be the same as the coefficient used a second pathologist.
  • some pathologists use 15000 as the coefficient while other pathologists may use 20000 as the coefficient.
  • the pathologists assume that the monolayer consists of uniformly distributed cells. As a result of which the pathologists multiply the estimated count with the coefficient. The result so obtained causes errors in the estimation.
  • Hemocytometers counting chambers that hold a specified volume of diluted blood and divide it with grid lines
  • This technique depends on manual counting and manual counting is subjected to sampling error because only few cells are counted compared to automated analysis.
  • the existing image-based analysis methodologies mainly employ cell segmentation, thresholding, feature extraction for estimating TC of blood cells. Further, the existing methodologies estimate count of blood cells by capturing random Field of Views (FoVs) of the blood smear for estimating TC of blood cells. Estimation of TC of blood cells using random fields-of-view results in a biased estimation of TC of blood cells.
  • FoVs Field of Views
  • the present disclosure discloses a method for estimating Total Count (TC) of blood cells in a blood smear.
  • the method comprises receiving, by a blood analyzer, a plurality of images of the blood smear captured from a monolayer of the blood smear. One or more sets of images of the plurality of images are characterized as corresponding regions. Each of the plurality of images comprises a plurality' of blood cells. Further, the method comprises determining a value of distribution of blood cells m each of the regions. Thereafter, the method comprises of identifying one or more uniform regions comprising uniformly distributed blood cells, from the regions. The one or more uniform regions are identified based on a comparison between the value of distribution of blood cells in each of the regions and a first threshold value.
  • TC Total Count
  • the method comprises identifying at least one uniform region from the one or more uniform regions based on a coefficient of variation of mean cell count of blood cells from each of the one or more uniform regions and a second threshold value.
  • the method comprises estimating the count of blood cells in the blood smear using the blood cells present in the at least one uniform region.
  • the present disclosure relates to a blood analyzer for estimating Total Count (TC) of blood cells m a blood smear.
  • the blood analyzer comprises a processor and a memory communicatively coupled with the processor, storing processor executable instructions, which, on execution causes the processor to receive a plurality of images of the blood smear captured from a monolayer of the blood smear.
  • One or more sets of images of the plurality of images are characterized as corresponding regions, wherein each of the plurality of images comprises a plurality of blood cells.
  • the processor is configured to determine a value of distribution of blood cells in each of the regions.
  • the processor is configured to identify one or more uniform regions comprising uniformly distributed blood cells, from the regions based on a comparison between the value of distribution of blood cells in each of the regions and a first threshold value. Furthermore, the processor is configured to identify at least one uniform region from the one or more uniform regions based on a coefficient of variation of mean cell count of blood cells from each of the one or more uniform regions and a second threshold value. Lastly, the processor is configured to estimate the count of blood ceils in the blood smear using the blood cells present in the at least one uniform region.
  • the present disclosure relates to a blood analysis system for estimating Total Count (TC) of blood cells in a blood smear.
  • the blood analysis system comprises an imaging unit for capturing a plurality of images of a monolayer of a blood smear.
  • the blood analysis system further comprises a blood analyzer for estimating total count of blood cells in the blood smear.
  • the blood analyzer receives a plurality of images of the blood smear captured from a monolayer of the blood smear.
  • One or more sets of images of the plurality of images are characterized as corresponding regions, wherein each of the plurality of images comprises a plurality of blood cells.
  • the blood analyzer is configured to determine a value of distribution of blood cells m each of the regions.
  • the blood analyzer is configured to identify one or more uniform regions comprising uniformly distributed blood cells, from the regions based on a comparison between the value of distribution of blood cells m each of the regions and a first threshold value. Furthermore, the blood analyzer is configured to identify at least one uniform region from the one or more uniform regions based on a coefficient of variation of mean ceil count of blood cells from each of the one or more uniform regions and a second threshold value. Lastly, the blood analyzer is configured to estimate the count of blood cells in the blood smear using the blood cells present in the at least one uniform region.
  • the blood analysis system further comprises a display unit for displaying the estimated total count of the blood cells.
  • FIG. 1 shows a diagram of a blood analyzer for estimating Total Count (TC) of blood cells in a blood smear, in accordance with some embodiments of the present disclosure
  • FIG. 2 shows an exemplary' block diagram of internal architecture of a blood analyzer for estimating Total Count (TC) of blood cells in a blood smear, in accordance with some embodiments of the present disclosure
  • FIG. 3 shows an exemplary flowchart illustrating method steps for estimating Total Count (TC) of blood cells in a blood smear, in accordance with some embodiments of the present disclosure
  • Figure 4A and Figure 4B shows different regions of a PBS, in accordance with some embodiments of the present disclosure
  • Figure 5 A and Figure SB represent images of FoV s chosen from different columns, in accordance with some embodiments of the present disclosure
  • Figures 6A, 6B, 7A and 7B illustrates a graph indicative of variation of the value of distribution of blood cells over one or more columns, in accordance with some embodiments of the present discl osure; and Figure 8 illustrates a block diagram of an exemplary computer system for implementing embodiments consistent with the present disclosure.
  • Embodiments of the present disclosure relate to a method and a system for estimating Total Count (TC) of blood cells in a blood smear.
  • a blood analyzer receives a plurality of images of the blood smear captured from a monolayer. One or more sets of images of the plurality of images are characterized as corresponding regions. Further, the system identifies one or more uniform regions from the regions, comprising uniformly distributed blood cells, based on a comparison between a value of distribution of blood ceils m each of the regions and a first threshold value. Thereafter, the system identifies at least one uniform region from the one or more uniform regions based on a coefficient of variation of mean cell count of blood cells from each of the one or more uniform regions. The system estimates the count of blood cells using the blood cells present in the at least one uniform region.
  • FIG. 1 shows a blood analyzer 100 for estimating Total Count (TC) of blood cells in a blood smear in accordance with some embodiments of the present disclosure.
  • the blood analyzer 100 comprises a microscopic system 101, a Peripheral Blood Smear (PBS)102, and a user interface 103.
  • the PBS 102 is a thin layer of blood of a blood sample smeared on a slide 104 and stained in such a way to allow the various blood cells to be examined microscopically.
  • the PBS 102 may correspond to a blood sample of a subject. In an embodiment, the subject may be a patient or any living being.
  • An image capturing unit (not shown) of the microscopic system 101 may capture high resolution images or enhanced microscopic images of the PBS 102.
  • a plurality' of images of the PBS 102 may be captured by the image capturing unit of the microscopic system 101.
  • the plurality of images may be referred as PBS images hereafter m the present disclosure.
  • Each of the PBS images is an area of the monolayer focused and captured by the image capturing unit.
  • each of the PBS images is a Field of View (FoV) of the image capturing unit.
  • the PBS images may be referred as plurality of FoVs.
  • the PBS images may be from a monolayer region of the PBS 102.
  • the blood analyzer 100 receives the FoVs and processes the FoVs for estimating the TC of blood cells in the PBS 102.
  • Each of the FoVs received from the microscopic system 101 may be a (Red, Green, Blue) RGB color image.
  • One or more set of FoVs of the FoVs received from the microscopic system 101 are characterizes as belonging to corresponding regions. Thus, forming a plurality of regions.
  • Each of the plurality of regions is characterized by one or more FoVs.
  • a plurality of blood cells (white blood cells and platelets) may be extracted from the one or more FoVs. Further, the blood analyzer 100 determines a value of distribution of blood cells using the blood cells present in each of the plurality of regions.
  • the blood analyzer 100 identifies one or more uniform regions from the plurality of regions.
  • the one or more uniform regions comprises uniformly distributed blood cells. Further, at least one uniform region from the one or more uniform regions is identified.
  • the blood analyzer 100 identifies number of blood cells in each of one or more FoVs in the at least one uniform region.
  • the blood cells may include but are not limited to, Red Blood Cells (RBCs), White Blood Cells (WBCs) and platelets.
  • the FoVs may include, the plurality of images of the PBS 102 in the monolayer of the PBS 102.
  • the microscopic system 101 may be any system which is configured to capture microscopic images of the PBS 102 and provide the microscopic images of the PBS 102 to the blood analyzer 100.
  • the microscopic system 101 may comprise a microscope, a stage and the image capturing unit for retrieving enhanced microscopic images of the PBS 102, The stage may be configured to hold the PBS 102.
  • the microscopic device 101 may be configured to focus on region of interest in the PBS 102.
  • Figure 4A indicates different regions of the PBS 102
  • the direction of smearing the blood on the PBS 102 is along an x-axis as shown in Figure 4A
  • the distribution of cells decreases as the blood is smeared along the x-axis.
  • the smeared blood sample is further classified into three regions namely, a clumped region, a monolayer region, and a feather edge region. As shown, density of blood decreases in the order of clumped region, monolayer region and feather edge region.
  • the clumped region (initial area) of the PBS 102 is a thicker region and the blood cells in the clumped region are overlap with each other (as illustrated in Figure 4).
  • the beginning of the smear is generally considered as the clumped region as the density of blood cells are more m the initial stage of smearing the blood sample.
  • the feather edge region is an area present at the end of the PBS 102 as illustrated in Figure 4A, where the blood cells are highly separated with wide gaps between cells or a group of cells.
  • the clumped region is generally the region obtained at the beginning of smearing the blood sample, along the direction of smearing. Therefore, the PBS images acquired from the clumped region and the feather edge region of the PBS results in over or under estimation of TC of blood cells respectively.
  • the microscopic system 101 may optimally scan the PBS to determine the monolayer region of the PBS 102.
  • the image capturing unit may be configured to capture enhanced microscopic images of the PBS 102 in the monolayer region.
  • the image capturing unit may be configured to capture enhanced microscopic images of the PBS 102 in the clumped side of the monolayer.
  • the images may be captured in the monolayer region proximal to the line dividing the monolayer region and the clumped region.
  • the region present at the transition from the clumped region to the monolayer region and an initial region of the monolayer may be termed as clumped side of the monolayer.
  • the platelet cell distribution and WBC distribution are found to be uniform in the initial part of the monolayer (clumped side of the monolayer). Thereby, for estimation of TC of WBC and platelets the FoVs may be selected from the clumped side of the monolayer.
  • the plurality of images covering different fields of the PBS 102 is captured by the imaging unit of the microscopic system 101 , for example one hundred twenty (120) images covering the monolayer region of the PBS 102, resulting in 120 FoVs.
  • the formats of the type of PBS images may be one of, but not limited to, Resource Interchange File Format (RIFF), Joint Photographic Experts Group (JPEG/JPG), BitMaP (BMP), Portable Network Graphics (PNG), Tagged Image File Format (TIFF), Raw image files (RAW), Digital Imaging and Communication (DICOM), Moving Picture experts group (MPEG), MPEG-4 Part 14 (MP4), etc.
  • the user interface 103 may comprise a display device, a report generation device or any other device capable of providing a notification or interacting with a user.
  • the user interface 103 may he used to notify the determined TC of blood cells to a clinical specialist examining the PBS images.
  • the user interface 103 may be a part of the blood analyzer 100 or may be associated with the blood analyzer 100.
  • the display device may be used to display the TC of blood cells estimated by the blood analyzer 100.
  • the display device may be one of, but not limited to, a monitor, a Liquid Crystal Display (LCD), a Light Emitting Diode (LED) display and/or any other module present which is capable of displaying the TC of blood cells.
  • LCD Liquid Crystal Display
  • LED Light Emitting Diode
  • the report generation unit may be used to generate a report comprising details of the TC of blood cells, estimated by the blood analyzer 100.
  • the microscopic system 101 may be separate unit associated with the blood analyzer 100.
  • the PBS images captured by the microscopic system 101 may be provided as an input to the blood analyzer 100 via a communication interface, for example wired and wireless communication interfaces.
  • FIG. 2 shows an exemplary block diagram of a blood analyzer 100 for estimating TC of blood cells in the PBS 102, in accordance with some embodiments of the present disclosure.
  • the blood analyzer 100 may include at least one processor 203 and a memory- 202 storing instructions executable by the at least one processor 203.
  • the processor 203 may comprise at least one data processor for executing program components for executing user or system-generated requests.
  • the memory 202 is communicatively coupled to the processor 203.
  • the blood analyzer 100 further comprises an Input/ Output (I/O) interface 201.
  • the I/O interface 201 is coupled with the processor 203 through which an input signal or/and an output signal is communicated.
  • the I/O interface 201 may provide the PBS images to the blood analyzer 100.
  • the I/O interface 201 couples the user interface 104 to the blood analyzer 100.
  • the processor 203 may implement machine learning models for analyzing the PBS images.
  • the processor 203 may implement any existing machine learning models winch may include, but are not limited to, decision tree learning, association rule learning, artificial neural networks or deep learning, inductive logic programming, support vector machines, clustering, Bayesian networks, reinforcement learning, representation learning, similarity- and metric learning, genetic algorithms, rule- based machine learning.
  • data 204 may be stored within the memory 202.
  • the data 204 may include, for example, training data 205, image data 206, parameters data 207 and other data 208.
  • the machine learning models may be trained to analyze the PBS images or captured FoVs using the training data 205.
  • the training data 205 may comprise a plurality of PBS images from the PBS images.
  • plurality of FoYs may be captured from the PBS 102.
  • random patches may be extracted from each of the plurality of FoVs.
  • the random patches are in a rectangular shape (columns).
  • the blood cells in each random patch are labeled as one of RBC, WBC and platelets by experts based on various parameters related to the blood cells.
  • the blood cells are labeled by the blood analyzer 100 using parameters of the blood cells. The labeled patches are used for training the deep learning model.
  • patch I is extracted from a PBS image.
  • the blood cell in patch 1 is found to be a WBC. Bence, the patch 1 is labeled as WBC by the expert.
  • the patch 1 is used for training the blood analyzer 100.
  • the blood analyzer 100 encounters a patch 2 similar to the patch 1, it may automatically classify the blood cell in patch 2 as WBC,
  • the blood analyzer 100 may be trained using a vast set of images from the training data 205 Thereby, the blood analyzer 100 may be able to efficiently identify RBCs, WBCs and platelets in the plurality of image patches extracted from each of the PBS images.
  • the blood analyzer 100 may discard the RBCs, platelets and any overlapping cell m each of the PBS images. Similarly, for estimating the TC of platelets, the blood analyzer 100 may discard the RBCs, WBCs and any overlapping cell in each of the PBS images.
  • the image data 206 refers to the properties of each of the PBS images.
  • the properties may include, but are not limited to, resolution or quality of the PBS images, sharpness of the PBS images, image size, and image format.
  • the parameters data 207 refers to the plurality' of statistical parameters determined by the blood analyzer 100 during estimation of TC of blood cells.
  • the parameters data 207 stores value corresponding to each of the plurality of statistical parameters computed for each of the PBS images. For instance, the parameters data 207 may store the mean cell count of blood cells in each FoV.
  • the other data 208 may include, but is not limited to weighing parameters data and threshold data.
  • the weighing parameters data refers to different parameters for assigning weight to each of the FoYs.
  • the weighing parameters data may be based on data present in the image data 206.
  • Each of the FoVs may be assigned a weight based on one or more parameters present in the weighing parameters data.
  • the threshold data refers to one or more threshold values used in determining uniformity of each of the plurality of regions.
  • the one or more threshold values may comprise a first threshold value and a second threshold value.
  • the data 204 in the memory 202 is processed by modules 209 of the blood analyzer 100.
  • the term module may refer to an application specific integrated circuit (ASIC), an electronic circuit, a field-programmable gate arrays (FPGA), Programmable System-on-Chip (PSoC), a combinational logic circuit, and/or other suitable components that provide the described functionality.
  • ASIC application specific integrated circuit
  • FPGA field-programmable gate arrays
  • PSoC Programmable System-on-Chip
  • a combinational logic circuit and/or other suitable components that provide the described functionality.
  • the modules 209 may include, for example, an image acquisition model 210, a FoV selection module 21 1 , a uniform column identification module 212, a uniform region identification module 213, TC estimation module 214 and other modules 215. it will he appreciated that such aforementioned modules 209 may he represented as a single module or a combination of different modules.
  • the uniform column identification module 212 and the uniform region identification module 213 may be part of the FoV selection module 211.
  • the image acquisition model 210 captures the FoVs (enhanced images of the PBS) from the image capturing unit of the microscopic system 101, for processing and for estimating TC of blood cells.
  • the FoV selection module 211 may select one or more FoVs from the plurality of regions.
  • the one or more FoVs are used for estimation of TC of blood cells.
  • the one or more regions may be referred to as one or more columns hereafter in the present disclosure.
  • Each FoV is an image of pre-defmed dimension and each FoV has respective x and y coordinates.
  • area of each FoV may be of a fixed dimension of 500 microns diameter.
  • FoVs having similar x coordinates and varying y coordinates are characterized as belonging to one region or a column.
  • each of the plurality of FoVs are characterized into corresponding region or column.
  • Each column may have one or more FoVs.
  • Each of the one or more FoVs comprises plurality of blood cells.
  • the blood cells may be one of RBC, WBC and platelet.
  • the 5 FoVs are characterized as belonging to a first column.
  • the FoV selection module chooses the one or more columns such that each of the one or more columns may comprise a pre-defmed number of FoVs. For an instance, a column having greater than 5 FoVs may be chosen. Selection of FoVs are performed to ensure unbiased estimation of TC of WBCs, TC of platelets, i.e., the one or more FoVs are selected such that each FoV comprises uniformly distributed blood cells.
  • the FoV selection module 21 1 may classify each of the plurality of blood cells in each of the one or more FoVs into one of RBC, WBC and platelet. For instance, consider a column which comprises 5 FoVs. For determining the TC of WBCs, the FoV selection module 211 uses the blood cells classified as WBCs in each of the 5 FoVs. Thus, the number of WBCs in each of the 5 FoVs may be determined. In an embodiment, the FoV selection module 211 may classify the blood cells according to one or more physiological/ hematological properties associated with the blood cells. For example, the FoV selection module 211 may classify blood cells as WBCs based on the size and structure.
  • the uniform column identification module 212 may identify uniform columns among the one or more columns.
  • a column is said to be uniformly distributed if the distribution of cells in the column is uniform.
  • a column is said to be uniformly distributed if the number of blood cells in each of the one or more FoVs identified in the given column is almost same.
  • a first column has 5 FoVs. Let a first FoV, second FoV and fifth FoV have 5 WBCs each, third FoV has 4 WBCs and a fourth FoV has 6 WBCs.
  • the first column is considered as a uniform column as the number of blood cells in each of one or more FoVs in the first column are almost the same.
  • the uniform column identification module 212 determines a value of distribution of blood cells in each of the one or more columns. In an embodiment, the uniform column identificationi on module 212 may determine coefficient of variation of blood cells count in each of the one or more columns. In another embodiment, the uniform column identification module 212 determines a Coefficient of Variation of blood cell count in each of the one or more columns. The uniform column identification module 212 identifies uniform columns from the one or more columns based on the SD of blood cell count in each of the one or more columns and a first threshold value.
  • the uniform region identification module 213 may identify at least one uniform region using the uniform columns.
  • the uniform region identification module 213 identifies the at least one uniform region based on a coefficient of variation of mean cell count of blood cells from each of the uniform columns and a second threshold value. For example, if a blood smear is divided into 8 columns, the uniform region identification module 213 may identify 6 columns having similar cell distribution.
  • the uniform region identification module 213 determines the mean cell count per column for each of the one or more uniform columns. Further, the uniform region identification module 213 determines a coefficient of variation of mean cell count per column and compares the coefficient of variation of mean ceil count per column with the second threshold value and determines at least one uniform region.
  • the TC estimation module 214 may determine the TC of blood cells using the blood cells present in the one or more FoVs of the at least one uniform region.
  • the TC estimation module 214 identifies number of blood cells in each of one or more FoVs (Field of Views) in the at least one uniform region.
  • the TC estimation module 214 performs statistical operations on the number of blood cells identified, for determining a set of variables and estimates the TC of blood cells based on the set of variables.
  • the TC estimation module 214 may be capable of estimating the TC of White Blood Cells (WBCs) and platelets.
  • WBCs White Blood Cells
  • the other modules 215, may include, but are not limited to, a report generation module.
  • the report generation module may be used to generate a report comprising details of the TC of blood cells estimated by the blood analyzer 100. It may further indicate the grade of the estimation of TC of blood cells.
  • the blood analyzer 100 may comprise only the modules as disclosed in Figure 2. In such cases, the blood analyzer 100 may be disposed in a remote server or in a cloud platform.
  • the blood analyzer 100 may also be referred as a blood analysis system.
  • the blood analysis system 100 may comprise the imaging unit or the microscopic system 101 and a display unit or the user interface 103.
  • the blood analysis system 100 may be portable and can provide an estimated TC instantly upon providing with a blood smear.
  • the blood analysis system 100 can be used in hospitals and climes.
  • An operator may place a blood sample beneath the notification unit 103 and initiate the blood analysis system 100.
  • the blood analysis system 100 may capture plurality of images, estimate the TC of blood cells and display the estimated results on the notification unit 103.
  • FIG. 3 show an exemplary flowchart illustrating method steps 300 for estimating TC of blood cells in a Peripheral Blood Smear (PBS), in accordance with some embodiments of the present disclosure.
  • PBS Peripheral Blood Smear
  • the method comprises one or more blocks for estimating TC of blood cells m PBS.
  • the method 300 may be described in the general context of machine executable instructions.
  • machine executable instructions can include routines, programs, objects, components, data structures, procedures, modules, and functions, which perform particular functions or implement particular abstract data types.
  • the order in which the method 300 is described is not intended to be construed as a limitation, and any number of the described method blocks can be combined in any order to implement the method. Additionally, individual blocks may be deleted from the methods without departing from the scope of the subject matter described herein. Furthermore, the method can be implemented in any suitable hardware, software, firmware, or combination thereof.
  • the plurality' of FoVs are acquired by the image acquisition module 210, from the microscopic system 101, for processing and determining TC of blood cells.
  • the plurality of FoVs may be captured by the imaging unit of the microscopic system 101 in the monolayer region of the PBS 102
  • Figure 4B indicates a representation of a portion of the PBS 102.
  • the PBS 102 as illustrated in Figure 4B comprises a monolayer 401 , a first column 402, FoV 403403. 401
  • the first column 402 comprises FoV al, FoV a2, FoV a3, FoV a4 and FoV a5.
  • FoV al , FoV a2, FoV a3, FoV a4 and FoV a5 are characterized to from the first column 402, Similarly, one or more sets of FoVs are characterized to from corresponding columns as illustrated in Figure 4B.
  • Each FoV present m the monolayer 401 may be collectively referred as one or more FoVs 403.
  • Each column present in the monolayer 401 may be collectively referred as one or more columns 402.
  • Each of the one or more FoVs 403 may be of the same pre-defined width.
  • each of the one or more columns 402 is a region of predefined width.
  • the predefined wadth may be 500 microns.
  • the regions representing the columns may take any other shape, for example, rectangular shape, square shape, etc.
  • the FoV selection module 211 may determine the value of distribution of blood cells in each of the one or more columns 402.
  • the FoV selection module 211 may extract and segment one or more Fo Vs 403 from each of the one or more columns 402.
  • Each of the one or more FoVs comprises plurality of blood cells.
  • the blood cells may be one of RBC, WBC and platelet.
  • the area of each of the one or more FoVs may be pre-defined.
  • the FoV selection module 211 may classify each of the plurality of blood cells in each of the one or more FoVs into one of RBC, WBC and piatelet.402.
  • Each of the one or more columns 402 as illustrated in Figure 4B comprises 5 FoVs.
  • the number of FoVs may not be limited to a specific value.
  • the one or more columns 402 may be chosen such that each of the one or more columns 402 contains at least a pre-defined number of FoVs. Selection of FoVs are performed to ensure unbiased estimation of TC of WBCs and TC of platelets.
  • the FoV selection module 21 1 uses the blood cells classified as WBCs in each of the 5 FoVs present in each of the one or more columns 402, Thus, the number of WBCs in each of the 5 FoVs may be determined for each column.
  • the extraction and classification of blood cells present in each of the plurality of FoVs may be performed using pre-trained Artificial Intelligence (AI) models or deep learning models.
  • the pre-trained AI models may be a combination of convolutional neural networks and statistical models.
  • the FoV selection module 211 may be trained using the training data 205.
  • the parameters considered for extraction and classification may be one of shape of the blood ceil, constituents of the blood cell, size of the blood cell and the like.
  • the FoV selection module 21 1 may neglect the overlapping blood cells.
  • Figure 5A and Figure 5B represent images of FoVs chosen by the FoV selection module 21 1 from two different columns. As illustrated in Figure 5A the blood cells inside in square boxes (numeral 501) indicate the WBCs identified by the FoV selection module 21 1 and the blood cells inside circular marking (numeral 502) indicate the platelets identified by the FoV selection module 21 1.
  • the uniform column identification module 212 determines the Coefficient of Variation (CV) of blood cell count in each of the one or more columns 402.
  • the CV is a value obtained by normalizing the Standard Deviation (SD) of blood cell count in each of the one or more columns 402 using the mean blood cell count per FoV in the corresponding column. The lower the value of CV of blood cell count in the given column, higher the uniformity of the blood cells in the given column.
  • the uniform column identification module 212 compares the CV of the blood cell count determined for each of the one or more columns 402 with the first threshold value. Each of the one or more columns 402 having the C V less than the first threshold is identified as the uniform column.
  • the uniform column identification module 212 identifies uniform columns from the one or more columns 402.
  • the first threshold value may be chosen depending on the amount of allowable error.
  • each of the identified uniform columns represents a uniform distribution of cells.
  • the uniform column identification module 212 ensures intra-column uniformity ie count of blood cells within the given column are uniform.
  • the number of WBCs in each of the 5 FoVs in a given column are 5 in number.
  • Each FoV among the 5 FoVs has 5 WBCs.
  • the WBCs are equally distributed in the given column.
  • the given column has intra-column uniformity.
  • Figure 6A illustrates a graph indicative of variation of the value of distribution of WBCs over one or more columns 402, The graph as illustrated in Figure 6A may be referred as a first graph hereafter in the present disclosure.
  • the first graph is used for estimating the TC of WBCs.
  • the first graph is a plot of value of CV of WBCs (y-axis) and specific columns (x-axis).
  • the horizontal line (601) in the first graph is indicative of the value of the first threshold value used for comparison.
  • the first graph is plotted for 10 columns (Cl, C2, . , CI O).
  • the first threshold value is set at 0.79. Each column among the 10 columns having a value of CV less than 0.79 may be chosen as the uniform column.
  • column 7 has a CV value greater than the first threshold value.
  • column 7 (C7) may be neglected.
  • the intra-column variation of WBCs in C7 is high and hence the FoVs present in the C7 are not considered for estimation of TC of WBCs.
  • Figure 6B illustrates a graph indicative of variation of the value of distribution of platelets over the one or more columns 402.
  • the graph as illustrated m Figure 6B may be referred as a second graph hereafter in the present disclosure.
  • the second graph is used for estimating the TC of platelets.
  • the second graph is a plot of value of CV of number of platelets (y-axis) and specific columns (x-axis).
  • the second graph indicates a variation of value of CV of platelets over the one or more columns 402.
  • the horizontal line (602) in the second graph is indicative of the value of the first threshold value used for comparison.
  • the number of columns for which the variation of CV is plotted m 10 columns (Cl, C2, . , CIO).
  • the first threshold value is set at 0.218.
  • Each column among the 10 columns having a value of CV less than 0.218 may be chosen as the uniform column. As illustrated in the second graph column 2 and columns 7-10 have a CV value greater than the first threshold value. Thus, column 2 (C2) and columns 7-10 (C7-C10) may be neglected.
  • the intra-column variation of platelets in C2 and C7-C10 is high and hence the FoVs present in the C2, C7-C10 are not considered for estimation of TC of platelets.
  • the uniform region identification module 213 may identify at least one uniform region using the uniform columns.
  • the uniform region may be consecutive group of columns or a set of columns.
  • the uniform region identification module 213 identifies the at least one uniform region based on a coefficient of variation of mean cell count of blood cells from each of the uniform columns and a second threshold value.
  • the uniform region identification module 213 determines the mean cell count per column for each of the one or more uniform columns. Further, the uniform region identification module 213 determines a variation of mean cell count per column and compares the variation of mean cell count per column with the second threshold value and determines at least one uniform region.
  • inter-column variation is examined.
  • the uniform columns are grouped in a selective manner.
  • the grouping is performed in a manner that each group of columns have similar (within a threshold of allowable error) mean (average) cell counts.
  • Each group represents a uniform region of the monolayer from where the one or more FoVs can be selected to estimate the TC of blood ceils.
  • Each group represents one uniform region, thus FoVs may not be selected from across groups (uniform regions).
  • Figure 7A illustrates a graph indicative of CV of mean ceil count value of WBCs over one or more columns.
  • the graph as illustrated in Figure 7A may be referred as a third graph hereafter m the present disclosure.
  • the third graph is a plot of value of CV of mean count of WBCs (y-axis) and specific columns (x-axis).
  • the third graph indicates a CV of mean count of WBCs over the one or more columns 402.
  • the horizontal line (701) in the third graph is indicative of the value of the second threshold value used for comparison.
  • the uniform columns determined in the previous step are combined in sequence for determining uniform region. For instance, consider 5 uniform columns are identified.
  • the CV of mean cell count/column is determined for combinations of columns.
  • the first column is combined with the second column and the CV of mean cell count of a first column and mean cell count of second column is determined and the CV is compared with the second threshold.
  • the first column and second column are combined with a third column.
  • the CV of mean cell count of the first column, the second column and the third column is determined and compared with the second threshold.
  • the columns identified to be non-uniform in the previous step are not considered during the step 304.
  • the second threshold value is set at 0.2, As illustrated in the third graph, the CV of mean cell count of WBCs by combining the first seven columns is less than the second threshold. Thus, the first seven columns may be considered as the at least one uniform region.
  • the inter-column variation of number of WBCs by combining the first eight columns is greater than the second threshold. Thus, the combination of first eight columns results in higher mter-column variation.
  • Figure 7B illustrates a graph indicative of CV of mean cell count value of platelets over one or more columns.
  • the graph as illustrated in Figure 7B may be referred as a fourth graph hereafter in the present disclosure.
  • the fourth graph is a plot of value of CV of mean count of Platelets(y-axis) and specific columns (x-axis).
  • the fourth graph indicates a CV of mean count of Platelets over the one or more columns 402
  • the horizontal line (702) in the fourth graph is indicative of the value of the second threshold value used for comparison in the fourth graph, the uniform columns determined in the previous step are combined in sequence for determining uniform region. For instance, consider 5 uniform columns are identified.
  • the CV of mean cell count/column is determined for combinations of columns.
  • the first column is combined with the second column and the CV of mean cell count of a first column and mean ceil count of second column is determined and the CV is compared with the second threshold.
  • the first column and second column are combined with a third column.
  • the CV of mean cell count of the first column, the second column and the tlurd column is determined and compared with the second threshold.
  • the columns identified to be non-uniform in the previous step are not considered during the step 304.
  • the second threshold value is set at 0.075.
  • the mean cell count of platelets by combining the first five columns is less than the second threshold.
  • the first five columns may be considered as the at least one uniform region.
  • the mter-column variation of number of platelets by combining the first five columns is greater than the second threshold.
  • the combination of first six, column results in higher inter column variation.
  • the TC estimation module 214 may determine the TC of blood cells using the blood cells present in the one or more FoVs of the at least one uniform region.
  • the TC estimation module 214 identifies number of blood cells in each of one or more FoVs (Field of Views) in the at least one uniform region.
  • the TC estimation module 214 performs statistical operations on the number of blood cells identified, for determining a set of variables and estimates the TC of blood cells based on the set of variables.
  • the TC estimation module 214 may be capable of estimating the TC of Whi te Blood Cells (WBCs) and platelets.
  • WBCs Whi te Blood Cells
  • the TC estimation module 214 calculates the statistical metrics like mean, median and percentiles using the cell count from the one or more FoVs chosen from the at least one uniform region.
  • the TC estimation module 214 determines correlation of each of the metrics with respect to the ground truth Total Count across the dataset (the training dataset of slides). Select the statistical metric having the highest correlation with the ground truth as the independent variable for estimating the TC of blood cells. Further the TC estimation module 214 may employ a regression model on the selected independent variable with respect to the ground truth Total Count (the training dataset of slides) to find the optimal coefficient for estimating the Total Count.
  • the performance of the blood analyzer 100 is analyzed, and blood analyzer 100 is validated on a set of 160 samples (for estimating TC of WBCs) and on a set of 196 slides (for estimating TC of platelets).
  • the slides are prepared using either of MGG stain and Leishman stain. Also, the samples are such that blood sample is taken from a given number of subjects and both MGG and Leishman stained PBS are prepared.
  • the Table 1 indicates the value of TC of WBCs estimated for three slides.
  • Table 2 As illustrated in Table 2, the results obtained from the above methodology are within the acceptable % difference of TC values of platelets in each blood slide present on the test dataset.
  • Table 2 indicates the Mean Absolute Difference (MAD) value of estimated TC of WBCs (over 160 samples) and estimated TC of platelets (over 196 samples).
  • the MAD value indicates mean of percentages of absolute difference between estimated value and actual value (ground truth derived from hematology analyzers). Pearson correlation is a measure of linear correlation between the estimated value and the actual value.
  • CLI A Clinical Laboratory' Improvements Amendments
  • the MAD value for estimation of TC of WBCs must be within 10% and the MAD value for estimation of TC of platelets must be within 25%.
  • the MAD values for TC ofWBCs and TC of platelets are within the CLIA guidelines. Further, the Pearson correlation and R squared correlation are in a higher range.
  • FIG. 8 illustrates a block diagram of an exemplary computer system 800 for implementing embodiments consistent with the present disclosure.
  • the computer system 800 is used to implement the blood analyzer 100.
  • the computer system 800 may comprise a central processing unit (“CPU” or“processor”) 802.
  • the processor 802 may comprise at least one data processor for executing program components for determining TC of blood cells in the PBS 102.
  • the processor 802 may include specialized processing units such as integrated system (bus) controllers, memory' management control units, floating point units, graphics processing units, digital signal processing units, etc.
  • the processor 802 may he disposed in communication with one or more input/output (I/O) devices (not shown) via I/O interface 801.
  • I/O input/output
  • the I/O interface 801 may employ communication protocols/methods such as, without limitation, audio, analog, digital, monoaural, RCA, stereo, IEEE- 1394, serial bus, universal serial bus (USB), infrared, PS/2, BNC, coaxial, component, composite, digital visual interface (DVl), high- definition multimedia interface (IIDMI), RF antennas, S-Video, VGA, IEEE 802. n /b/g/n/x, Bluetooth, cellular (e.g., code-division multiple access (CDMA), high-speed packet access (HSPA+), global system for mobile communications (GSM), long-term evolution (LTE), WiMax, or the like), etc.
  • CDMA code-division multiple access
  • HSPA+ high-speed packet access
  • GSM global system for mobile communications
  • LTE long-term evolution
  • WiMax wireless wide area network
  • the computer system 800 may communicate with one or more I/O devices.
  • the input device 88 may be an antenna, keyboard, mouse, joystick, (infrared) remote control, camera, card reader, fax machine, dongle, biometric reader, microphone, touch screen, touchpad, trackball, stylus, scanner, storage device, transceiver, video device/source, etc.
  • the input device 88 may be the microscopic system 81.
  • the output device 811 may be a printer, fax machine, video display (e.g., cathode ray tube (CRT), liquid crystal display (LCD), light-emitting diode (LED), plasma, Plasma display panel (PDP), Organic light-emitting diode display (OLED) or the like), audio speaker, etc.
  • video display e.g., cathode ray tube (CRT), liquid crystal display (LCD), light-emitting diode (LED), plasma, Plasma display panel (PDP), Organic light-emitting diode display (OLED) or the like
  • audio speaker e.g., a printer, fax machine, video display (e.g., cathode ray tube (CRT), liquid crystal display (LCD), light-emitting diode (LED), plasma, Plasma display panel (PDP), Organic light-emitting diode display (OLED) or the like), audio speaker, etc.
  • CTR cathode ray tube
  • LCD liquid crystal display
  • the computer system 800 is connected to a server 812. through a communication network 809.
  • the server 812 may implement image processing tools used by the computer system 800.
  • the processor 802 may be disposed in communication with the communication network 809 via a network interface 803
  • the network interface 803 may communicate with the communication network 809.
  • the network interface 803 may employ connection protocols including, without limitation, direct connect Ethernet (e.g., twisted pair 8/80/800 Base T), transmission control protocol/internet protocol (TCP/IP), token ring, IEEE 802.11 a/b/g/n/x, etc.
  • the communication network 809 may include, without limitation, a direct interconnection, local area network (LAN), wide area network (WAN), wireless network (e.g., using Wireless Application Protocol), the Internet, etc.
  • connection protocols include, but not limited to, direct connect, Ethernet (e.g., twisted pair 8/80/800 Base T), transmission control protocol/mternet protocol (TCP/IP), token ring, IEEE 802.11a/b/g/n/x, etc.
  • the communication network 809 includes, but is not limited to, a direct interconnection, an e-commerce network, a peer to peer (P2P) network, local area network (LAN), wide area network (WAN), wireless network (e.g., using Wireless Application Protocol), the Internet, Wi-Fi and such.
  • the first network and the second network may either be a dedicated network or a shared network, which represents an association of the different types of networks that use a variety' of protocols, for example, Hypertext Transfer Protocol (HTTP), Transmission Control Protocol/Internet Protocol (TCP/IP), Wireless Application Protocol (WAP), etc., to communicate with each other.
  • the first network and the second network may include a variety of network devices, including routers, bridges, servers, computing devices, storage devices, etc.
  • the processor 802 may be disposed in communication with a memory 805 (e.g., RAM, ROM, etc. not shown in figure 5) via a storage interface 804.
  • the storage interface 804 may connect to memory 805 including, without limitation, memory drives, removable disc drives, etc., employing connection protocols such as serial advanced technology atachment (SATA), Integrated Drive Electronics (IDE), IEEE- 1394, Universal Serial Bus (USB), fiber channel, Small Computer Systems Interface (SCSI), etc.
  • the memory drives may further include a drum, magnetic disc drive, magneto-optical drive, optical drive, Redundant Array of Independent Discs (RAID), solid-state memory devices, solid-state drives, etc.
  • the memory 805 may store a collection of program or database components, including, without limitation, user interface 806, an operating system 807, web server 808 etc.
  • computer system 800 may store user/application data 806, such as, the data, variables, records, etc., as described in this disclosure.
  • databases may be implemented as fault-tolerant, relational, scalable, secure databases such as Oracle ® or Sybase®.
  • the operating system 807 may facilitate resource management and operation of the computer system 800.
  • Examples of operating systems include, without limitation, APPLE MACINTOSH 11 OS X, UNIX R , UNIX-like system distributions (E.G., BERKELEY SOFTWARE.
  • DISTRIBUTIONTM (BSD), FREEBSDTM, NETBSDTM, QPENBSDTM, etc.), LINUX DISTRIBUTIONSTM (E.G., RED HATTM, UBUNTUTM, KUBUNTUTM, etc.), IBMTM OS/2, MICROSOFTTM WINDOWSTM (XPTM, VISTATM/7/8, 8 etc.), APPLE* IOSTM, GOGGLE* ANDROIDTM, BLACKBLRRY R OS, or the like.
  • the computer system 800 may implement a web browser 808 stored program component.
  • the web browser 808 may be a hypertext viewing application, for example MICROSOFT* INTERNET EXPLORERTM, GOOGLE* CHROMETM 0 , MOZILLA* FTREFOXTM, APPLE* SAFARITM, etc. Secure web browsing may be provided using Secure Hypertext Transport Protocol (HTTPS), Secure Sockets Layer (SSL), Transport Layer Security (TLS), etc.
  • Web browsers 808 may utilize facilities such as AJAXTM, DHTMLTM, ADOBE* FLASHTM, JAVASCRIPTTM, JAVATM, Application Programming Interfaces (APIs), etc.
  • the computer system 800 may implement a mail server stored program component.
  • the mail server may be an Internet mail server such as Microsoft Exchange, or the like.
  • the mail server may utilize facilities such as ASPTM, ACTIVEXTM, ANSITM C++/C#, MICROSOFT*, .NETTM, CGI SCRIPTSTM, JAVATM, JAVASCRIPTTM, PERLTM, PHPTM, PYTHONTM, WEBOBJECTSTM, etc.
  • the mail server may utilize communication protocols such as Internet Message Access Protocol (IMAP), Messaging Application Programming Interface (MAPI), MICROSOFT* exchange, Post Office Protocol (POP), Simple Mail Transfer Protocol (SMTP), or the like.
  • IMAP Internet Message Access Protocol
  • MAPI Messaging Application Programming Interface
  • MICROSOFT* exchange
  • Post Office Protocol POP
  • Simple Mail Transfer Protocol SMTP
  • the computer system 800 may implement a mail client stored program component.
  • the mail client may be a mail viewing application, such as APPLE* MAILTM, MICROSOFT* ENTOURAGETM, MICROSOFT* OUTLOOKTM, MOZILLA* THUNDERBIRDTM, etc.
  • one or more computer-readable storage media may be utilized in implementing embodiments consistent with the present disclosure.
  • a computer-readable storage medium refers to any type of physical memor on which information or data readable by a processor may be stored.
  • a computer-readable storage medium may store instructions for execution by one or more processors, including instructions for causing the processor(s) to perform steps or stages consistent with the embodiments described herein.
  • Computer-readable medium should be understood to include tangible items and exclude carrier waves and transient signals, i.e., be non-transitory. Examples include Random Access Memory (RAM), Read-Only Memory (ROM), volatile memory, nonvolatile memory, hard drives, CD ROMs, DVDs, flash drives, disks, and any other known physical storage media.
  • RAM Random Access Memory
  • ROM Read-Only Memory
  • volatile memory volatile memory
  • nonvolatile memory hard drives
  • CD ROMs, DVDs compact flash drives
  • disks and any other known physical storage media.
  • Embodiments of the present disclosure relate to a method and system for estimating the TC of blood cells in the PBS.
  • the system acquires the plurality of images from the monolayer region of the PBS, thereby producing an unbiased estimation of the TC of blood cells.
  • the method and system are proficient and robust in estimating TC of blood cells efficiently.
  • the method as disclosed determines both intra-column uniformity and inter- column uniformity for determining uniform distribution of cells.
  • the method and system is smear agnostic.
  • the system is robust and proficient in estimating the TC of blood cells even when different image capturing devices are used to capture images of the PBS.
  • the described operations may be implemented as a method, system or article of manufacture using standard programming and/or engineering techniques to produce software, firmware, hardware, or any combination thereof.
  • the described operations may be implemented as code maintained in a“non-transitory computer readable medium”, where a processor may read and execute the code from the computer readable medium.
  • the processor is at least one of a microprocessors and a processor capable of processing and executing the queries
  • a non-transitory computer readable medium may comprise media such as magnetic storage medium (e.g., hard disk drives, floppy disks, tape, etc.), optical storage (CD-ROMs, DVDs, optical disks, etc.), volatile and non-volatile memory devices (e.g., EEPROMs, ROMs, PROMs, RAMs, DRAMs, SRAMs, Flash Memory, firmware, programmable logic, etc.), etc.
  • non-transitory computer-readable media comprise all computer-readable media except for a transitory.
  • the code implementing the described operations may further be implemented in hardware logic (e.g., an integrated circuit chip, Programmable Gate Array (PGA), Application Specific Integrated Circuit (ASIC), etc.).
  • the code implementing the described operations may be implemented in“transmission signals”, where transmission signals may propagate through space or through a transmission media, such as an optical fiber, copper wire, etc.
  • the transmission signals in which the code or logic is encoded may further comprise a wireless signal, satellite transmission, radio w3 ⁇ 4ves, infrared signals, Bluetooth, etc.
  • the transmission signals in which the code or logic is encoded is capable of being transmitted by a transmitting station and received by a receiving station, where the code or logic encoded m the transmission signal may be decoded and stored in hardware or a non-transitory computer readable medium at the receiving and transmitting stations or devices.
  • An“article of manufacture” comprises non-transitory computer readable medium, hardware logic, and/or transmission signals in which code may be implemented.
  • a device in which the code implementing the described embodiments of operations is encoded may comprise a computer readable medium or hardware logic.

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Abstract

Les modes de réalisation de la présente invention concernent un procédé et un système d'estimation du nombre total (TC) de cellules sanguines dans un frottis sanguin. Un analyseur de sang reçoit une pluralité d'images du frottis sanguin capturées à partir d'une monocouche. Un ou plusieurs ensemble(s) d'images de la pluralité des images est/sont caractérisé(s) en tant que régions correspondantes. Le système identifie une ou plusieurs région(s) uniforme(s) parmi les régions, comprenant des cellules sanguines uniformément distribuées, sur la base d'une comparaison entre une valeur de distribution de cellules sanguines dans chacune des régions et une première valeur seuil. Le système identifie au moins une région uniforme parmi la ou les région(s) uniforme(s) sur la base d'un coefficient de variation du nombre moyen de cellules sanguines provenant de chaque région uniforme. Le système estime le nombre de cellules sanguines à l'aide des cellules sanguines présentes dans la ou les région(s) uniforme(s).
PCT/IN2019/050197 2018-04-09 2019-03-11 Procédé et système d'estimation du nombre total de cellules sanguines dans un frottis sanguin WO2019198094A1 (fr)

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