Method for analyzing cells, method for training deep learning algorithm, cell analyzer, device for training deep learning algorithm, cell analysis program, and deep learning algorithm training program

Abstract

To determine the type of cells which an existing scattergram cannot determine.SOLUTION: The present invention relates to a method for analyzing cells which analyzes a cell in a living body sample by a deep learning algorithm of a neural network structure, the method including the steps of flowing the cell in a flow passage, acquiring a signal intensity related to individual cells passing through the flow passage, inputting the numerical value data corresponding to the signal intensity related to the acquired individual cells into the deep learning algorithm, and determining the type of cells of which signal intensity has been acquired on a cell-by-cell basis on the basis of the result output from the deep learning algorithm.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] This specification discloses a method for analyzing cells, a method for training a deep learning algorithm, a cell analysis device, a training device for a deep learning algorithm, a cell analysis program, and a training program for a deep learning algorithm. [Background technology]

[0002] Patent Document 1 discloses a cell analyzer that analyzes the types of cells, such as blood cells, contained in peripheral blood. In such a cell analyzer, for example, light is irradiated onto cells in peripheral blood flowing through a flow cell, and the signal intensity of scattered light or fluorescence obtained from the cells irradiated with light is acquired. Peak values ​​of the signal intensity acquired from multiple cells are extracted and displayed on a scattergram. Cluster analysis is performed on the multiple cells on the scattergram, and the type of cells belonging to each cluster is identified.

[0003] Patent Document 2 describes a method for classifying cell types using an imaging flow cytometer. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Publication No. 180836 / 1983 [Patent Document 2] International Publication No. 2018 / 203568 Summary of the Invention [Problem to be solved by the invention]

[0005] In this way, when trying to identify the type of cell based on the scattergram, for example, if cells that do not normally appear in the peripheral blood of healthy individuals, such as blast cells or lymphoma cells, are present in the sample, they may be classified as normal cells in the cluster analysis.

[0006] In addition, because cluster analysis is a statistical analysis method, when the number of cells displayed in the scattergram is small, cluster analysis can be difficult.

[0007] Furthermore, in the method described in Patent Document 2, in order to determine the type of cells more accurately, a method is adopted in which the cells flowing in the flow cell are imaged and a structure illumination is irradiated. Therefore, there is a problem that the detection system used to obtain a scattergram in the past cannot be used.

[0008] An object of one embodiment of the present invention is to further improve accuracy so that different types of cells appearing in the same cluster can be determined, and to provide a method for determining the type of cells that can be applied to a measuring device that has conventionally measured a scattergram. [Means for solving the problem]

[0009] 4, one embodiment of this embodiment relates to a cell analysis method for analyzing cells contained in a biological sample using a deep learning algorithm (60) with a neural network structure. The cell analysis method includes flowing cells through a flow path, acquiring signal intensities of individual cells passing through the flow path, inputting numerical data corresponding to the acquired signal intensities of the individual cells into the deep learning algorithm (60), and determining the type of the cell whose signal intensity has been acquired for each cell based on the results output from the deep learning algorithm (60). According to this embodiment, it is possible to determine the type of cells that cannot be determined by conventional cell analyzers.

[0010] In the cell analysis method, preferably, the signal intensity is acquired for each cell passing through a predetermined position in the flow channel at multiple time points while the cell passes through the predetermined position, and the acquired signal intensity is stored in association with information regarding the time points at which the signal intensity was acquired. By adopting such an embodiment, it is possible to determine the type of cell that cannot be determined by conventional cell analyzers. Furthermore, by acquiring information regarding the time points at which the signal intensity was acquired, it is possible to synchronize data when multiple signals are received from one cell.

[0011] In the cell analysis method, preferably, acquisition of signal intensities at multiple time points is started when the signal intensity of each cell reaches a predetermined value, and ends a predetermined time after the start of acquisition of the signal intensities. By adopting such an embodiment, more accurate determination can be performed. Also, the amount of data to be acquired can be reduced.

[0012] In the cell analysis method, the signal is preferably an optical signal or an electrical signal.

[0013] More preferably, the optical signal is a signal obtained by irradiating light onto individual cells passing through the flow cell. Furthermore, the predetermined position is a position within the flow cell (4113, 551) where light is irradiated onto the cells. Even more preferably, the light is laser light, and the optical signal is at least one selected from a scattered light signal and a fluorescent signal. Even more preferably, the optical signal is a side scattered light signal, a forward scattered light signal, and a fluorescent signal. By adopting such an embodiment, the accuracy of determining the type of cell in the flow cytometer can be improved.

[0014] In the cell analysis method, the numerical data corresponding to the signal intensity input to the deep learning algorithm (60) includes information combining the signal intensities of the side scattered light signal, the forward scattered light signal, and the fluorescent signal acquired at the same time for each cell. By adopting such an embodiment, it is possible to further improve the accuracy of the determination by the deep learning algorithm.

[0015] In the analysis method, when the signal is an electrical signal, the measurement unit includes a sheath flow electrical resistance detection unit. By adopting such an embodiment, the type of cell can be determined based on data measured by the sheath flow electrical resistance method.

[0016] In the cell analysis method, a deep learning algorithm (60) calculates, for each cell, the probability that the cell whose signal intensity has been acquired belongs to a plurality of cell types linked to an output layer (60b) of the deep learning algorithm (60). Preferably, the deep learning algorithm (60) outputs a label value 82 of the cell type to which the cell whose signal intensity has been acquired has the highest probability of belonging. By adopting such an embodiment, it is possible to present the determination result to a user.

[0017] In the cell analysis method, the number of cells belonging to each of a plurality of cell types is counted based on the label value of the cell type to which the cell whose signal intensity has been acquired has the highest probability of belonging, and the results are output. Alternatively, the proportion of cells belonging to each of a plurality of cell types is calculated based on the label value of the cell type to which the cell whose signal intensity has been acquired has the highest probability of belonging, and the results are output. By adopting such an embodiment, the proportion of each cell type contained in a biological sample can be obtained.

[0018] In the cell analysis method, the biological sample is preferably a blood sample. More preferably, the type of cells includes at least one type selected from the group consisting of neutrophils, lymphocytes, monocytes, eosinophils, and basophils. Even more preferably, the type of cells includes at least one type selected from the group consisting of the following (a) and (b). Here, (a) is an immature granulocyte, and (b) is at least one type of abnormal cell selected from the group consisting of nucleated erythrocytes and megakaryocytes selected from tumor cells, lymphoblasts, plasma cells, atypical lymphocytes, proerythroblasts, basophilic erythroblasts, polychromatic erythroblasts, normochromatic erythroblasts, promegaloblasts, basophilic megaloblasts, polychromatic megaloblasts, and normochromatic megaloblasts. By adopting such an embodiment, it is possible to determine the type of immature granulocytes and abnormal cells contained in the blood sample.

[0019] In addition, in the cell analysis method, when the biological sample is a blood sample and the cell type includes abnormal cells, if there is a cell determined to be an abnormal cell by the deep learning algorithm (60), the processing unit (20) may output a message indicating that the biological sample contains abnormal cells.

[0020] In the cell analysis method, the biological sample may be urine. By adopting such an embodiment, it becomes possible to determine cells contained in urine.

[0021] One embodiment of this embodiment relates to a method for analyzing cells contained in a biological sample, in which cells are flowed through a flow path, and signal intensities related to scattered light and fluorescence are acquired for each cell passing through a predetermined position in the flow path at multiple time points while the cell passes through the predetermined position, and the type of each cell is determined based on the result of recognizing the acquired signal intensities at the multiple time points for each cell as a pattern. According to this embodiment, it is possible to determine the type of cell that cannot be determined by a conventional cell analyzer.

[0022] One embodiment of this embodiment relates to a method for training a deep learning algorithm (50) of a neural network structure for analyzing cells in a biological sample. Cells contained in the biological sample are passed through a cell detection flow path in a measurement unit capable of detecting cells individually, and numerical data corresponding to the signal intensity acquired for each cell passing through the flow path is input to an input layer of the deep learning algorithm as first training data, and information on the type of cell corresponding to the cell whose signal intensity has been acquired is input to the deep learning algorithm as second training data. According to this embodiment, it is possible to generate a deep learning algorithm for determining the type of each individual cell that cannot be determined by a conventional cell analyzer.

[0023] The present embodiment relates to a cell analyzer (4000, 4000') that uses a deep learning algorithm (60) having a neural network structure to determine the type of cells for each cell. The cell analyzer (4000, 4000') includes a processing unit (20), which acquires signal intensity for each cell when cells contained in a biological sample flow through a cell detection flow path of a measurement unit capable of detecting cells individually pass through the flow path, inputs numerical data corresponding to the acquired signal intensity for each cell into the deep learning algorithm (60), and determines the type of the cell whose signal intensity has been acquired for each cell based on the result output from the deep learning algorithm. According to the present embodiment, it is possible to determine the type of cells that cannot be determined by conventional cell analyzers.

[0024] Furthermore, the cell analyzer (4000, 4000') includes a measuring unit (400) that acquires signal intensity of individual cells when cells contained in a biological sample pass through a cell detection flow path of a measuring unit capable of detecting individual cells. According to this embodiment, the cell analyzer including the measuring unit can determine the type of cells that cannot be determined by conventional cell analyzers.

[0025] One embodiment of this embodiment relates to a training device (100) for training a deep learning algorithm (50) of a neural network structure for analyzing cells in a biological sample. The training device includes a processing unit (100), which causes cells contained in the biological sample to flow through a cell detection flow path in a measurement unit capable of detecting cells individually, inputs numerical data corresponding to the signal intensity acquired for each cell passing through the flow path as first training data to an input layer of the deep learning algorithm, and inputs information on the cell type corresponding to the cell whose signal intensity has been acquired as second training data to the deep learning algorithm. According to this embodiment, it is possible to generate a deep learning algorithm for determining the type of cells that cannot be determined by conventional cell analyzers.

[0026] An embodiment of this embodiment relates to a computer program for analyzing cells contained in a biological sample using a deep learning algorithm (60) with a neural network structure. The computer program causes the processing unit (20) to execute a process including the steps of: passing cells contained in the biological sample through a cell detection flow path in a measurement unit capable of detecting cells individually, acquiring signal intensities of individual cells passing through the flow path, inputting numerical data corresponding to the acquired signal intensities of individual cells into the deep learning algorithm, and determining the type of the cell whose signal intensity has been acquired for each cell based on the result output from the deep learning algorithm. According to this embodiment, the cell analyzer equipped with the measurement unit can determine the type of cells that cannot be determined by conventional cell analyzers.

[0027] An embodiment of this embodiment relates to a computer program for training a deep learning algorithm (50) of a neural network structure for analyzing cells in a biological sample. The computer program causes the processing unit (10) to execute a process including the steps of flowing cells contained in the biological sample through a cell detection flow path in a measurement unit capable of detecting cells individually, inputting numerical data corresponding to the signal intensity acquired for each cell passing through the flow path into an input layer of the deep learning algorithm as first training data, and inputting information on the cell type corresponding to the cell whose signal intensity has been acquired into the deep learning algorithm as second training data. According to this embodiment, it is possible to generate a deep learning algorithm for determining the type of cells that cannot be determined by conventional cell analyzers. Effect of the Invention

[0028] It is possible to determine the types of cells that cannot be determined by conventional cell analysis methods, thereby improving the accuracy of cell determination. [Brief description of the drawings]

[0029] [Figure 1](a) An example of a scattergram of healthy blood is shown. (b) An example of a scattergram of unhealthy blood is shown. (c) An example of a conventional scattergram is shown. (d) An example of waveform data is shown. (f) A schematic diagram of the deep learning algorithm is shown. (g) An example of cell determination is shown. [Diagram 2] An example of a method for generating training data will be described below. [Diagram 3] Examples of label values ​​are as follows: [Figure 4] An example of a method for generating analysis data is shown below. [Diagram 5] 1 shows an example of the external appearance of a cell analyzer. [Figure 6] 1 shows a block diagram of a measurement unit. [Figure 7] 1 shows a schematic example of the optical system of a flow cytometer. [Figure 8] 1 shows a schematic example of a sample preparation section of a measurement unit. [Figure 9] (a) A schematic example of the red blood cell / platelet detection unit is shown, and (b) A histogram of cells detected by the sheath flow electrical resistance method is shown. [Figure 10] 1 shows a block diagram of a measurement unit. [Figure 11] 1 shows a schematic example of the optical system of a flow cytometer. [Figure 12] 1 shows a schematic example of a sample preparation section of a measurement unit. [Figure 13] 1 shows a schematic example of a waveform data analysis system. [Figure 14] FIG. 2 shows a block diagram of a vendor-side device. [Figure 15] 1 shows a block diagram of a user side device. [Figure 16] 1 shows an example of a functional block diagram of a vendor-side device. [Figure 17] 13 shows an example flowchart of the operation of a processing unit for generating training data. [Figure 18] The following are schematic diagrams for explaining a neural network. (a) shows a schematic diagram showing an overview of a neural network. (b) shows a schematic diagram showing the operations at each node. (c) shows a schematic diagram showing the operations between nodes. [Figure 19] 1 shows an example of a functional block diagram of a user side device. [Figure 20] 13 shows an example flowchart of the operation of a processing unit to generate data for analysis. [Figure 21] 1 shows a schematic example of a waveform data analysis system. [Figure 22] FIG. 1 shows a functional block diagram of a waveform data analysis system. [Figure 23] 1 shows a schematic example of a waveform data analysis system. [Figure 24] FIG. 1 shows a functional block diagram of a waveform data analysis system. [Diagram 25] An example of output data is shown below. [Figure 26] The confusion matrix between the judgment results using the reference method and the judgment results using the deep learning algorithm is shown. [Figure 27] (a) shows the ROC curve for neutrophils, (b) shows the ROC curve for lymphocytes, and (c) shows the ROC curve for monocytes. [Figure 28] (a) shows the ROC curve for eosinophils, (b) shows the ROC curve for basophils, and (c) shows the ROC curve for control blood (CONT). DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0030] Hereinafter, an overview and embodiments of the present invention will be described in detail with reference to the accompanying drawings. In the following description and drawings, the same reference numerals indicate the same or similar components, and therefore, the description of the same or similar components will be omitted.

[0031] [1. Cell analysis method] The present embodiment relates to a method for analyzing cells contained in a biological sample, in which numerical data corresponding to the signal intensity of each cell is input to a deep learning algorithm having a neural network structure, and the type of the cell whose signal intensity is obtained is determined for each cell based on the results output from the deep learning algorithm.

[0032] An example of the outline of this embodiment will be described with reference to FIG. 1. In FIG. 1, (a) shows a scattergram obtained by measuring the signal intensity of fluorescence and scattered light of individual cells contained in healthy blood as a biological sample using a flow cytometer. The horizontal axis shows the side scattered light, and the vertical axis shows the signal intensity of side fluorescence. Similarly to (a), (b) shows a scattergram obtained by measuring the signal intensity of side fluorescence and side scattered light of individual cells contained in unhealthy blood as a biological sample using a flow cytometer. The diagrams shown in (a) and (b) are used for white blood cell classification using a conventional flow cytometer. However, when unhealthy blood cells are generally contained in blood, they are mixed with healthy blood cells, so that the dots of healthy blood cells and the dots of unhealthy blood cells may overlap as shown in (c).

[0033] In this embodiment, we focused on data indicating signal intensity derived from individual cells obtained from one cell when creating a scattergram. In FIG. 1(d), FSC indicates data indicating signal intensity of forward scattered light, SSC indicates waveform data of side scattered light, and the diagram indicated by SFL indicates data indicating signal intensity of side fluorescent light. Here, FIG. 1(d) is shown as a drawn waveform for convenience, but in this embodiment, the data shown by the waveform is intended to be a data group whose elements are a value indicating the time when the signal intensity was obtained and a value indicating the signal intensity at that time, and is not intended to be the shape of the drawn waveform itself. Note that the data group refers to sequence data or matrix data. In FIG. 1(d), acquisition of signal intensity is started when each cell passes a predetermined position, and measurement is started after a predetermined time.

[0034] In this embodiment, the waveform data for each type of cell is trained on deep learning algorithms 50 and 60 shown in FIG. 1(f), and the determination result (FIG. 1(g)) of the type of each cell contained in the biological sample is derived based on the results output from the trained deep learning algorithm. Hereinafter, each cell in the biological sample that is subjected to analysis for the purpose of determining the type of cell is also referred to as the "cell to be analyzed." In other words, the biological sample may contain multiple cells to be analyzed. The multiple cells may include multiple types of cells to be analyzed.

[0035] The biological sample may be a biological sample collected from a subject. For example, the biological sample may include blood, such as peripheral blood, venous blood, and arterial blood, urine, and body fluids other than blood and urine. Body fluids other than blood and urine may include bone marrow, ascites, pleural fluid, and cerebrospinal fluid. Hereinafter, body fluids other than blood and urine may be simply referred to as "body fluids". There are no limitations on the blood sample, so long as it is in a state in which the number of cells can be counted and the type of cells can be determined. The blood is preferably peripheral blood. For example, the blood may be peripheral blood collected using an anticoagulant such as ethylenediaminetetraacetate (sodium salt or potassium salt) or heparin sodium. The peripheral blood may be collected from an artery or a vein.

[0036] The type of cells to be determined in this embodiment is based on the type of cells based on morphological classification, and varies depending on the type of biological sample. When the biological sample is blood and the blood is collected from a healthy individual, the type of cells to be determined in this embodiment includes nucleated cells such as red blood cells and white blood cells, platelets, etc. The nucleated cells include neutrophils, lymphocytes, monocytes, eosinophils, and basophils. The neutrophils include segmented neutrophils and band-shaped neutrophils. On the other hand, when the blood is collected from a non-healthy individual, the nucleated cells may include at least one type selected from the group consisting of immature granulocytes and abnormal cells. Such cells are also included in the type of cells to be determined in this embodiment. The immature granulocytes may include cells such as metamyelocytes, myelocytes, promyelocytes, and myeloblasts.

[0037] In addition to normal cells, the nucleated cells may contain abnormal cells that are not contained in the peripheral blood of healthy individuals. Examples of abnormal cells are cells that appear when a patient is afflicted with a specific disease, such as tumor cells. In the case of the hematopoietic system, the specific disease may be a disease selected from the group consisting of leukemias such as myelodysplastic syndrome, acute myeloblastic leukemia, acute myeloblastic leukemia, acute promyelocytic leukemia, acute myelomonocytic leukemia, acute monocytic leukemia, erythroleukemia, acute megakaryoblastic leukemia, acute myelogenous leukemia, acute lymphocytic leukemia, lymphoblastic leukemia, chronic myelogenous leukemia, or chronic lymphocytic leukemia, malignant lymphomas such as Hodgkin's lymphoma and non-Hodgkin's lymphoma, and multiple myeloma.

[0038] Furthermore, abnormal cells may include cells not normally found in peripheral blood of healthy individuals, such as lymphoblasts, plasma cells, atypical lymphocytes, reactive lymphocytes, erythroblasts, which are nucleated red blood cells such as proerythroblasts, basophilic erythroblasts, polychromatic erythroblasts, normochromatic erythroblasts, promegaloblasts, basophilic megaloblasts, polychromatic megaloblasts, and normochromatic megaloblasts, and megakaryocytes, including micromegakaryocytes.

[0039] Furthermore, when the biological sample is urine, the types of cells to be determined in this embodiment may include red blood cells, white blood cells, epithelial cells such as transitional epithelium and squamous epithelium, etc. Abnormal cells may include bacteria, fungi such as filamentous fungi and yeast, tumor cells, etc.

[0040] When the biological sample is a body fluid that does not normally contain blood components, such as ascites, pleural fluid, or cerebrospinal fluid, the types of cells may include red blood cells, white blood cells, and large cells. Here, "large cells" refer to cells that are detached from the inner lining of a body cavity or the peritoneum of an internal organ and are larger than white blood cells, and specifically include mesothelial cells, histiocytes, tumor cells, etc.

[0041] In the case where the biological sample is bone marrow, the type of cells to be determined in this embodiment may include mature blood cells and immature blood cells as normal cells. Mature blood cells include nucleated cells such as red blood cells and white blood cells, and platelets. Nucleated cells such as white blood cells include neutrophils, lymphocytes, plasma cells, monocytes, eosinophils, and basophils. Neutrophils include segmented neutrophils and band-shaped neutrophils. Immature blood cells include hematopoietic stem cells, immature granulocytic cells, immature lymphocytic cells, immature monocytic cells, immature erythroid cells, megakaryocytic cells, mesenchymal cells, and the like. Immature granulocytes may include metamyelocytes, myelocytes, promyelocytes, myeloblasts, and the like. Immature lymphocytic cells include lymphoblasts, and the like. Immature monocytic cells include monoblasts, and the like. Immature erythroid cells include nucleated erythrocytes such as proerythroblasts, basophilic erythroblasts, polychromatic erythroblasts, normochromatic erythroblasts, promegaloblasts, basophilic megaloblasts, polychromatic megaloblasts, normochromatic megaloblasts, etc. Megakaryocytic cells include megakaryoblasts, etc.

[0042] Examples of abnormal cells that may be contained in the bone marrow include hematopoietic tumor cells selected from the group consisting of the above-mentioned myelodysplastic syndrome, acute myeloblastic leukemia, acute myeloblastic leukemia, acute promyelocytic leukemia, acute myelomonocytic leukemia, acute monocytic leukemia, erythroleukemia, acute megakaryoblastic leukemia, acute myeloid leukemia, acute lymphocytic leukemia, lymphoblastic leukemia, chronic myeloid leukemia, and chronic lymphocytic leukemia, malignant lymphomas such as Hodgkin's lymphoma and non-Hodgkin's lymphoma, and multiple myeloma, and metastatic tumor cells of malignant tumors occurring in organs other than the bone marrow.

[0043] FIG. 1 shows an example in which an optical signal (forward scattered light signal, side scattered light signal, side fluorescent light signal) is used as the signal. However, the signal may be, for example, an electrical signal. The optical signal is a light signal emitted from the cell when the cell is irradiated with light. The optical signal may include at least one type selected from a scattered light signal and a fluorescent signal. In this specification, the light may be irradiated so as to be direct light toward the flow of cells in a flow channel. "Forward" refers to the traveling direction of light emitted from a light source. "Forward" may include a forward low angle where the light receiving angle is approximately 0 to 5 degrees when the angle of the irradiated light is 0 degrees, and / or a forward high angle where the light receiving angle is approximately 5 to 20 degrees. "Side" is not limited as long as it does not overlap with "forward". "Side" may include a light receiving angle of approximately 25 degrees to 155 degrees, preferably approximately 45 degrees to 135 degrees, and more preferably approximately 90 degrees when the angle of the irradiated light is 0 degrees. In this embodiment, regardless of the type of signal, a data group (sequence data or matrix data, preferably one-dimensional sequence data) whose elements are a value indicating the time when the signal strength was acquired and a value indicating the signal strength at that time may be collectively referred to as waveform data.

[0044] In the cell analysis method of the present embodiment, the determination of the type of cell is not limited to a method using a deep learning algorithm. Signal intensities may be acquired for each cell passing through a predetermined position in a flow channel at multiple time points while the cell passes through the predetermined position, and the type of cell may be determined based on the result of recognizing the acquired signal intensities at multiple time points for each cell as a pattern. The pattern may be recognized as a numerical pattern of the signal intensities at multiple time points, or may be recognized as a shape pattern when the signal intensities at multiple time points are plotted as a graph. When recognizing as a numerical pattern, the cell type can be determined by comparing the numerical pattern of the cell to be analyzed with a numerical pattern of a cell type already known. The comparison between the numerical pattern of the cell to be analyzed and the numerical pattern of the control can be performed using, for example, Spearman's rank correlation, z score, etc. The cell type can be determined by comparing the graph shape pattern of the cell to be analyzed with the graph shape pattern of a cell type already known. The comparison of the graph shape pattern of the cell to be analyzed with the graph shape pattern of a cell type that is already known may be performed, for example, using geometric shape pattern matching or a feature descriptor such as the SIFT Descriptor. <Overview of cell analysis methods>

[0045] Next, a method for generating training data 75 and a method for analyzing waveform data will be described using the examples shown in FIGS.

[0046] <Generating training data> The example shown in FIG. 2 is an example of a method for generating training waveform data used to train a deep learning algorithm for determining the types of white blood cells, immature granulocytes, and abnormal cells. The waveform data 70a of forward scattered light, the waveform data 70b of side scattered light, and the waveform data 70c of side fluorescent light are linked to the cells of the training subject. The training waveform data 70a, 70b, and 70c acquired from the cells of the training subject may be waveform data obtained by measuring cells whose types of cells based on morphological classification are known by flow cytometry. Alternatively, waveform data of cells whose types have already been determined from a scattergram of a healthy person may be used. In addition, a pool of waveform data of cells acquired from multiple people may be used as waveform data of cells whose types of cells of a healthy person have been determined. It is preferable that the specimens for acquiring the training waveform data 70a, 70b, and 70c are processed from a sample containing the same type of cells as the cells of the training subject by the same specimen processing method as the specimen containing the cells of the training subject. In addition, it is preferable that the training waveform data 70a, 70b, and 70c are acquired under the same conditions as the acquisition conditions of the cells to be analyzed. The training waveform data 70a, 70b, and 70c can be obtained in advance for each cell by, for example, a known flow cytometry or sheath flow electrical resistance method. Here, when the training target cells are red blood cells or platelets, the training data becomes waveform data obtained by a sheath flow electrical resistance method, and the waveform data may be a type obtained from the electrical signal intensity.

[0047] In the example shown in FIG. 2, training waveform data 70a, 70b, and 70c obtained by flow cytometry using Sysmex XN-1000 are used. The training waveform data 70a, 70b, and 70c are examples of waveform data obtained at multiple points in time for one training subject cell at regular intervals from when the forward scattered light reaches a predetermined threshold and when the signal intensity of the forward scattered light, the signal intensity of the side scattered light, and the signal intensity of the side fluorescent light are started to be obtained until the acquisition is completed after a predetermined time. Examples of waveform data obtained at multiple points in time at regular intervals include 1024 points at 10 nanosecond intervals, 128 points at 80 nanosecond intervals, and 64 points at 160 nanosecond intervals. Each waveform data is obtained for each cell passing through the flow channel by flowing cells contained in a biological sample into a cell detection channel in a measurement unit capable of individually detecting cells provided in a flow cytometer, a sheath flow electrical resistance type measurement device, and the like. Specifically, at multiple time points during the passage of one training cell through a predetermined position in the flow channel, a data group having elements of a value indicating the time when the signal intensity was acquired and a value indicating the signal intensity at that time is acquired for each signal, and is used as training waveform data 70a, 70b, 70c. The information on the time point is not limited as long as it can be stored so that the processing unit 10, 20 described later can determine how much time has passed since the acquisition of the signal intensity started. For example, the information on the time point may be the time from the start of the measurement, or the number of the point. The signal intensity is preferably stored in the storage unit 13, 23 or memory 12, 22 described later together with information on the time point when the signal intensity was acquired.

[0048] The training waveform data 70a, 70b, and 70c in FIG. 2 are expressed by raw data values, and are, for example, sequence data 72a of forward scattered light, sequence data 72b of side scattered light, and sequence data 72c of side fluorescence. The sequence data 72a, 72b, and 72c are synchronized with the time when the signal intensity is acquired for each training cell, and are sequence data 76a of forward scattered light, sequence data 76b of side scattered light, and sequence data 76c of side fluorescence. That is, the second numerical value from the left of 76a is the signal intensity at the time t=0 when the measurement is started, which is 10. Similarly, the second numerical values ​​from the left of 76b and 76c are the signal intensity at the time t=0 when the measurement is started, which are 50 and 100, respectively. In addition, adjacent cells in each of 76a, 76b, and 76c store signal intensities at 10 nanosecond intervals. The sequence data 76a, 76b, and 76c are combined with a label value 77 indicating the type of the cell to be trained, and input to the deep learning algorithm 50 as training data 75 so that three signal intensities (signal intensity of forward scattered light, signal intensity of side scattered light, and signal intensity of side fluorescence) at the same time are set. For example, when the cell to be trained is a neutrophil, the sequence data 76a, 76b, and 76c are given a label value 77 indicating that the cell is a neutrophil, and training data 75 is generated. FIG. 3 shows an example of the label value 77. Since the training data 75 is generated for each type of cell, a label value 77 that differs depending on the type of cell is given to the data. Here, synchronization of the time at which the signal intensities are acquired refers to matching the measurement points so that the time from the start of measurement is combined at the same time in the sequence data 72a of the forward scattered light, the sequence data 72b of the side scattered light, and the sequence data 72c of the side fluorescence. In other words, the forward scattered light sequence data 72a, the side scattered light sequence data 72b, and the side fluorescent light sequence data 72c are intended to be adjusted so that they are the signal intensities acquired at the same time point for one cell passing through the flow cell. The measurement start time may be the time point when the signal intensity of the forward scattered light exceeds a predetermined threshold such as a threshold, but a threshold for the signal intensity of other scattered light or fluorescent light may also be used. A threshold may also be set for each sequence data.

[0049] The sequence data 76a, 76b, and 76c may be the acquired signal intensity values ​​as they are, or may be subjected to processing such as noise removal, baseline correction, normalization, etc., as necessary. In this specification, "numerical data corresponding to signal intensity" may include the acquired signal intensity values ​​themselves, and values ​​that have been subjected to noise removal, baseline correction, normalization, etc., as necessary.

[0050] <Overview of deep learning> An overview of neural network training will be described with reference to FIG. 2 as an example. The neural network 50 is preferably a convolutional neural network. The number of nodes in the input layer 50a of the neural network 50 corresponds to the number of sequences included in the waveform data of the input training data 75. The training data 75 is combined so that the signal strengths of the sequence data 76a, 76b, and 76c are acquired at the same time, and is input to the input layer 50a of the neural network 50 as first training data. The label values ​​77 of each waveform data of the training data 75 are input to the output layer 50b of the neural network as second training data to train the neural network 50. The reference symbol 50c in FIG. 2 indicates an intermediate layer.

[0051] <How to analyze waveform data> FIG. 4 shows an example of a method for analyzing waveform data of a cell to be analyzed. In the method for analyzing waveform data, analysis data 85 is generated from waveform data 80a of forward scattered light, waveform data 80b of side scattered light, and waveform data 80c of side fluorescence acquired from a cell to be analyzed. The analysis waveform data 80a, 80b, and 80c can be acquired, for example, using a known flow cytometer. In the example shown in FIG. 4, the analysis waveform data 80a, 80b, and 80c are acquired using a Sysmex XN-1000 in the same manner as the training waveform data 70a, 70b, and 70c. When the analysis waveform data 80a, 80b, and 80c are expressed by raw data values, they are, for example, forward scattered light sequence data 82a, side scattered light waveform data 82b, and side fluorescence waveform data 82c.

[0052] Regarding the generation of the analysis data 85 and the generation of the training data 75, it is preferable to make at least the acquisition conditions and the conditions for generating data to be input to the neural network from each waveform data, etc., the same. The sequence data 82a, 82b, and 82c are synchronized with the time point at which the signal intensity is acquired for each cell to be trained, and become sequence data 86a (forward scattered light), sequence data 86b (side scattered light), and sequence data 86c (side fluorescence). The sequence data 86a, 86b, and 86c are combined so that the three signal intensities (signal intensity of forward scattered light, signal intensity of side scattered light, and signal intensity of side fluorescence) at the same time are combined to form a set, and are input to the deep learning algorithm 60 as the analysis data 85.

[0053] When the analysis data 85 is input to the input layer 60a of the neural network 60 constituting the trained deep learning algorithm 60, the output layer 60b outputs the probability that the cell of the analysis target from which the analysis data 85 is obtained belongs to each of the cell types input as training data. Reference numeral 60c in FIG. 4 indicates an intermediate layer. Furthermore, it may be determined that the cell of the analysis target from which the analysis data 85 is obtained belongs to the classification with the highest value among these probabilities, and a label value 82 or the like linked to the type of the cell may be output. The output analysis result 83 on the cell may be data in which the label value is replaced with information indicating the type of the cell (for example, a term, etc.) in addition to the label value itself. FIG. 4 shows an example in which the deep learning algorithm 60 outputs the label value "1" to which the cell of the analysis target from which the analysis data 85 is obtained has the highest probability of belonging based on the analysis data 85, and further, character data of "neutrophil" corresponding to this label value is output as the analysis result 83 on the cell. The label value may be output by the deep learning algorithm 60, or another computer program may output the most preferred label value based on the probability calculated by the deep learning algorithm 60.

[0054] [2. Cellular analyzers and the measurement of biological samples using cellular analyzers] The waveform data of this embodiment can be acquired in the first cell analyzer 4000 or the second cell analyzer 4000'. FIG. 5(a) shows the appearance of the cell analyzer 4000. FIG. 5(b) shows the appearance of the cell analyzer 4000'. In FIG. 5(a), the cell analyzer 4000 includes a measurement unit (also called a measurement section) 400 and a processing unit 300 for controlling the setting of the measurement conditions of the sample in the measurement unit 400 and the measurement. In FIG. 5(b), the cell analyzer 4000' includes a measurement unit (also called a measurement section) 500 and a processing unit 300 for controlling the setting of the measurement conditions of the sample in the measurement unit 500 and the measurement. The measurement units 400, 500 and the processing unit 300 can be connected to each other by wire or wirelessly so that they can communicate with each other. Below, configuration examples of the measurement units 400, 500 are shown, but the embodiment of this embodiment is not limited to the following examples. The processing unit 300 may be shared with the vendor device 100 or the user device 200 described below. The block diagram of the processing unit 300 is similar to that of the vendor device 100 or the user device 200.

[0055] <First cell analyzer and preparation of measurement samples> (Configuration of the first measuring unit) A configuration example (measuring unit 400) in which the first measuring unit 400 is a flow cytometer for detecting nucleated cells in a blood sample will be described with reference to Figs. 6 to 8.

[0056] 6 shows an example of a block diagram of the measurement unit 400. As shown in this figure, the measurement unit 400 includes a detection section 410 that detects blood cells, an analog processing section 420 for the output of the detection section 410, a measurement unit control section 480, a display / operation section 450, a sample preparation section 440, and an apparatus mechanism section 430. The analog processing section 420 performs processing including noise removal on the analog electrical signal input from the detection section, and outputs the processed result to an A / D conversion section 482 as an electrical signal.

[0057] The detection unit 410 includes at least a nucleated cell detection unit 411 for detecting nucleated cells such as white blood cells, a red blood cell / platelet detection unit 412 for measuring the number of red blood cells and the number of platelets, and a hemoglobin detection unit 413 for measuring the amount of hemoglobin in blood as necessary. The nucleated cell detection unit 411 is composed of an optical detection unit, and more specifically, includes a configuration for performing detection by flow cytometry.

[0058] 6, the measurement unit control section 480 includes an A / D conversion section 482, a digital value calculation section 483, and an interface section 489 that connects to the processing unit 300. The measurement unit control section 480 further includes an interface section 486 that is interposed between the measurement unit control section 480 and the display / operation section 450, and an interface section 488 that is interposed between the measurement unit control section 480 and the device mechanism section 430.

[0059] The digital value calculation unit 483 is connected to an interface unit 489 via an interface unit 484 and a bus 485. The interface unit 489 is connected to the display / operation unit 450 via the bus 485 and an interface unit 486, and is connected to the detection unit 410, the device mechanism unit 430, and the sample preparation unit 440 via the bus 485 and an interface unit 488.

[0060] The A / D conversion unit 482 converts the received light signal, which is an analog signal output from the analog processing unit 420, into a digital signal and outputs it to the digital value calculation unit 483. The digital value calculation unit 483 performs a predetermined calculation process on the digital signal output from the A / D conversion unit 482. The predetermined calculation process includes, for example, a process of acquiring each waveform data at multiple points in time at regular intervals for one training target cell from the time when the forward scattered light reaches a predetermined threshold value until the acquisition is completed after a predetermined time, a process of extracting a peak value of the waveform data, and the like, but is not limited to this. Then, the digital value calculation unit 483 outputs the calculation result (measurement result) to the processing unit 300 via the interface unit 484, the bus 485, and the interface unit 489.

[0061] The processing unit 300 is connected to the digital value calculation unit 483 via an interface unit 484, a bus 485, and an interface unit 489, and the processing unit 300 can receive the calculation results output from the digital value calculation unit 483. The processing unit 300 also controls the device mechanism unit 430, which is composed of a sampler (not shown) that automatically supplies sample containers, a fluid system for sample preparation and measurement, and other controls.

[0062] The nucleated cell detection unit 411 passes a measurement sample containing cells through a flow path for cell detection, irradiates light on the cells flowing through the flow path for cell detection, and measures scattered light and fluorescence generated from the cells. The red blood cell / platelet detection unit 412 passes a measurement sample containing cells through a flow path for cell detection, measures the electrical resistance of the cells flowing through the flow path for cell detection, and detects the volume of the cells.

[0063] In this embodiment, the measurement unit 400 preferably includes a flow cytometer and / or a sheath flow electrical resistance type detection unit. In Fig. 6, the nucleated cell detection unit 411 may be a flow cytometer. In Fig. 6, the red blood cell / platelet detection unit 412 may be a sheath flow electrical resistance type detection unit. Here, nucleated cells may be measured by the red blood cell / platelet detection unit 412, and red blood cells and platelets may be directly measured by the nucleated cell detection unit 411.

[0064] Flow cytometer As shown in Figure 7, in measurement using a flow cytometer, when cells contained in a measurement sample pass through a flow cell (sheath flow cell) 4113 provided in the flow cytometer, a light source 4111 irradiates light onto the flow cell 4113, and the scattered light and fluorescence emitted from the cells in the flow cell 4113 are detected by this light.

[0065] In this embodiment, the scattered light is not particularly limited as long as it can be measured by a commonly available flow cytometer. For example, the scattered light can be forward scattered light (for example, light receiving angle of about 0 to 20 degrees) and side scattered light (light receiving angle of about 90 degrees). It is known that side scattered light reflects internal information of a cell, such as the cell nucleus and granules, and forward scattered light reflects information on the size of the cell. In this embodiment, it is preferable to measure the forward scattered light intensity and side scattered light intensity as the scattered light intensity.

[0066] Fluorescence is light emitted from a fluorescent dye bound to nucleic acids in a cell when excitation light of an appropriate wavelength is applied to the fluorescent dye. The excitation light wavelength and the receiving light wavelength depend on the type of fluorescent dye used.

[0067] 7 shows an example of the configuration of the optical system of the nucleated cell detection unit 411. In this figure, light emitted from a laser diode, which is a light source 4111, is irradiated via an irradiation lens system 4112 onto cells passing through a flow cell 4113.

[0068] In this embodiment, the light source 4111 of the flow cytometer is not particularly limited, and a light source 201 having a wavelength suitable for exciting a fluorescent dye is selected. As such a light source 201, for example, a semiconductor laser including a red semiconductor laser and / or a blue semiconductor laser, a gas laser such as an argon laser or a helium-neon laser, a mercury arc lamp, etc. are used. In particular, a semiconductor laser is preferable because it is very inexpensive compared to a gas laser.

[0069] As shown in FIG. 7, forward scattered light emitted from a particle passing through a flow cell 4113 is received by a forward scattered light receiving element 4116 via a condenser lens 4114 and a pinhole portion 4115. The forward scattered light receiving element 4116 may be a photodiode or the like. The side scattered light is received by a side scattered light receiving element 4121 via a condenser lens 4117, a dichroic mirror 4118, a bandpass filter 4119, and a pinhole portion 4120. The side scattered light receiving element 4121 may be a photodiode, a photomultiplier, or the like. The side fluorescent light is received by a side fluorescent light receiving element 4122 via the condenser lens 4117 and a dichroic mirror 4118. The side fluorescent light receiving element 4122 may be an avalanche photodiode, a photomultiplier, or the like.

[0070] The received light signals output from the light receiving parts 4116, 4121 and 4122 are subjected to analog processing such as amplification and waveform processing by the analog processing part 420 shown in FIG.

[0071] Returning to Fig. 6, the measurement section 400 may include a sample preparation section 440 that prepares a measurement sample. The sample preparation section 440 is controlled by a measurement unit information processing section 481 via an interface 488 and a bus 485. Fig. 8 shows how the sample preparation section 440 provided in the measurement section 400 prepares a measurement sample by mixing a blood sample, a staining reagent, and a hemolysis reagent, and how the obtained measurement sample is measured by a nucleated cell detection section.

[0072] In Fig. 8, a blood sample in a sample container 00a is aspirated from an aspirating pipette 601. The blood sample measured by the aspirating pipette 601 is mixed with a predetermined amount of diluent and transported to a reaction chamber 602. A predetermined amount of hemolysis reagent is added to the reaction chamber 602. A predetermined amount of staining reagent is supplied to the reaction chamber 602 and mixed with the above mixture. By reacting the mixture of the blood sample, staining reagent and hemolysis reagent in the reaction chamber 602 for a predetermined time, red blood cells in the blood sample are hemolyzed and a measurement sample in which nucleated cells are stained with a fluorescent dye is obtained.

[0073] The obtained measurement sample is sent to a flow cell 4113 in the nucleated cell detection unit 411 together with a sheath liquid (for example, Cell Pack (II), manufactured by Sysmex Corporation), and is measured in the nucleated cell detection unit 411 by flow cytometry.

[0074] Sheath flow type electrical resistance detector As shown in FIG. 9(a), the red blood cell / platelet detector 412, which is a sheath flow type electrical resistance detector, includes a chamber wall 412a, an aperture section 412b for measuring the electrical resistance of cells, a sample nozzle 412c for supplying a sample, and a recovery tube 412d for recovering cells that have passed through the aperture section 412b. The area around the sample nozzle 412c and the recovery tube 412d in the chamber wall 412a is filled with sheath fluid. The dashed arrow indicated by the symbol 412s indicates the direction in which the sheath fluid flows. Red blood cells 412e and platelets 412f discharged from the sample nozzle pass through the aperture section 412b while being enveloped in the flow of sheath fluid 412s. A constant DC voltage is applied to the aperture section 412b, and it is controlled so that a constant current flows while only the sheath fluid flows. Since cells do not easily conduct electricity, that is, have a large electrical resistance, when a cell passes through the aperture portion 412b, the electrical resistance changes, and therefore the number of times the cell has passed through and the electrical resistance can be detected at the aperture portion 412b. Since the electrical resistance increases in proportion to the volume of the cell, the measurement unit information processing unit 481 shown in FIG. 6 calculates the volume of the cell that has passed through the aperture portion 412b, and displays the count number of cells for each volume as a histogram shown in FIG. 9(b) on the display unit / operation unit 450 shown in FIG. 6, or can send it to the processing unit 300 via the bus 487 and the interface unit 489. The signal related to the electrical resistance value is sent to the processing unit 300 as signal intensity through processing by the analog processing unit 420, the A / D conversion unit 482, and the digital value calculation unit 483 shown in FIG. 6, similar to the processing for the signal obtained from the light described above.

[0075] <Second cell analyzer and measurement of biological samples in the second cell analyzer> (Configuration of measuring device 2) As a configuration example of the second cell analyzer 4000', an example of a block diagram in which the measuring unit 500 is a flow cytometer for measuring urine samples or body fluid samples is shown.

[0076] Fig. 10 is an example of a block diagram of the measurement unit 500. In Fig. 10, the measurement unit 500 includes a specimen distribution section 501, a sample preparation section 502, and an optical detection section 505, an amplifier circuit 550 that amplifies the output signal of the optical detection section 505 (an output signal amplified by a preamplifier), a filter circuit 506 that performs a filter process on the output signal from the amplifier circuit 550, an A / D conversion section 507 that converts the output signal (analog signal) of the filter circuit 506 into a digital value, a digital value processing circuit 508 that performs a predetermined process on the digital value, a memory 509 connected to the digital value processing circuit 508, a microcomputer 511 connected to the specimen distribution section 501, the sample preparation section 502, the amplifier circuit 550, the digital value processing circuit 508, and a storage device 511a, and a LAN adapter 12 connected to the microcomputer 511. The processing unit 300 is connected to the measurement unit 500 by a LAN cable via the LAN adapter 12, and the processing unit 300 analyzes the measurement data acquired by the measurement unit 500. The optical detection section 505, the amplifier circuit 550, the filter circuit 506, the A / D converter 507, the digital value processing circuit 508, and the memory 509 constitute an optical measurement section 510 that measures the measurement sample and generates measurement data.

[0077] FIG. 11 is a diagram showing the configuration of the optical detection section 505 of the measurement unit 500. In FIG. 11, a condenser lens 552 focuses the laser light emitted from a semiconductor laser light source 553, which is a light source, on a flow cell 551, and a focusing lens 554 focuses the forward scattered light emitted from the solid components in the measurement sample on a forward scattered light receiving section 555. Another focusing lens 556 focuses the side scattered light and fluorescence emitted from the solid components on a dichroic mirror 557. The dichroic mirror 557 reflects the side scattered light to a side scattered light receiving section 558 and transmits the fluorescence to a fluorescence receiving section 559. These optical signals reflect the characteristics of the solid components in the measurement sample. Then, the forward scattered light receiving section 555, the side scattered light receiving section 558, and the fluorescence receiving section 559 convert the optical signals into electrical signals and output a forward scattered light signal, a side scattered light signal, and a fluorescence signal, respectively. These outputs are amplified by a preamplifier and then subjected to processing at the next stage. In addition, the forward scattered light receiving unit 555, the side scattered light receiving unit 558, and the fluorescent light receiving unit 559 can each be switched between low sensitivity output and high sensitivity output by switching the driving voltage. This sensitivity switching is performed by the microcomputer 11 described later. In this embodiment, a photodiode is used as the forward scattered light receiving unit 555, and a photomultiplier tube may be used as the side scattered light receiving unit 558 and the fluorescent light receiving unit 55, or a photodiode may be used as the side scattered light receiving unit 558 and the fluorescent light receiving unit 559. The fluorescent light signal output from the fluorescent light receiving unit 559 is amplified by a preamplifier and then given to two branched signal channels. The two signal channels are each connected to the amplifier circuit 550 described above in FIG. 10. The fluorescent light input to one of the signal channels is amplified to high sensitivity by the amplifier circuit 550.

[0078] (Preparation of measurement samples) Fig. 12 is a diagram showing a schematic functional configuration of the sample preparation section 502 and the optical detection section 505 shown in Fig. 10. The specimen distribution section 501 shown in Figs. 10 and 12 includes an aspirating tube 517 and a syringe pump. The specimen distribution section 501 aspirates a specimen (urine or body fluid) 00b through the aspirating tube 517 and dispenses it into the specimen preparation section 502. The specimen preparation section 502 includes a reaction tank 512u and a reaction tank 512b. The specimen distribution section 501 distributes a quantified amount of the measurement sample to each of the reaction tanks 512u and 512b.

[0079] In the reaction tank 512u, the distributed biological sample is mixed with a first reagent 519u as a diluent and a third reagent 518u containing a dye. The pigment contained in the third reagent 518u stains the formed elements in the specimen. When the biological sample is urine, the sample prepared in the reaction tank 512u is used as a first measurement sample for analyzing relatively large formed elements in urine, such as red blood cells, white blood cells, epithelial cells, and tumor cells. When the biological sample is a body fluid, the sample prepared in the reaction tank 512u is used as a third measurement sample for analyzing red blood cells in the body fluid.

[0080] Meanwhile, in the reaction vessel 512b, the distributed biological sample is mixed with a second reagent 519b as a diluent and a fourth reagent 518b containing a dye. As described below, the second reagent 519b has a hemolytic effect. The pigment contained in the fourth reagent 518b stains the formed elements in the specimen. When the biological sample is urine, the sample prepared in this reaction vessel 512b becomes a second measurement sample for analyzing bacteria in the urine. When the biological sample is a body fluid, the sample prepared in the reaction vessel 512b becomes a fourth measurement sample for analyzing nucleated cells (white blood cells and large cells) and bacteria in the body fluid.

[0081] A tube extends from the reaction tank 512u to a flow cell 551 of the optical detection unit 505, so that a measurement sample prepared in the reaction tank 512u can be supplied to the flow cell 551. An electromagnetic valve 521u is provided at the outlet of the reaction tank 512u. A tube also extends from the reaction tank 512b, and this tube is connected to the middle of the tube extending from the reaction tank 512u. This allows a measurement sample prepared in the reaction tank 512b to be supplied to the flow cell 551. An electromagnetic valve 521b is provided at the outlet of the reaction tank 512u.

[0082] A tube extending from the reaction chambers 512u and 512b to the flow cell 551 branches just before the flow cell 551, and the branched end is connected to the syringe pump 520a. In addition, an electromagnetic valve 521c is provided between the syringe pump 520a and the branching point.

[0083] The tubes extend from the reaction vessels 512u and 512b, and branch further from the connection point to the branch point, which is connected to the syringe pump 520b. In addition, an electromagnetic valve 521d is provided between the branch point of the tube extending to the syringe pump 520b and the connection point.

[0084] Further, a sheath fluid storage unit 522 that stores sheath fluid is connected to the sample preparation unit 502, and this sheath fluid storage unit 522 is connected to the flow cell 551 by a tube. A compressor 522a is connected to the sheath fluid storage unit 522, and when the compressor 522a is driven, compressed air is supplied to the sheath fluid storage unit 522, and the sheath fluid is supplied from the sheath fluid storage unit 522 to the flow cell 551.

[0085] Of the two types of suspensions (measurement samples) prepared in the reaction vessels 512u and 512b, the suspension in the reaction vessel 512u (first measurement sample when the biological sample is urine, or third measurement sample when the biological sample is body fluid) is first introduced to the optical detection unit 505, where it forms a thin stream surrounded by sheath liquid in the flow cell 551, and is irradiated with laser light. Thereafter, the suspension in the reaction vessel 512b (second measurement sample when the biological sample is urine, or fourth measurement sample when the biological sample is body fluid) is similarly introduced to the optical detection unit 505, where it forms a thin stream in the flow cell 551, and is irradiated with laser light. Such operations are automatically performed by operating the solenoid valves 521a, 521b, 521c, 521d and the drive unit 503 under the control of the microcomputer 511 (control unit) described later.

[0086] The first to fourth reagents will be described in detail. The first reagent 519u is a reagent whose main component is a buffer, contains an osmotic pressure compensating agent so as to obtain a stable fluorescent signal without hemolyzing red blood cells, and is adjusted to 100 to 600 mOsm / kg so as to have an osmotic pressure suitable for classification and measurement. It is preferable that the first reagent 519u does not have a hemolytic effect on red blood cells in urine.

[0087] Unlike the first reagent 519u, the second reagent 519b has a hemolytic effect. This is to increase the permeability of the fourth reagent 518b (described later) to the cell membrane of bacteria and to accelerate the staining. Furthermore, it is also to shrink impurities such as mucus threads and red blood cell fragments. The second reagent 519b contains a surfactant to obtain a hemolytic effect. Various surfactants such as anionic, nonionic, and cationic surfactants are used, but cationic surfactants are particularly suitable. Since the surfactant can damage the cell membrane of bacteria, the dye contained in the fourth reagent 518b can efficiently stain the nucleic acid of the bacteria. As a result, the measurement of bacteria can be performed by a short staining process.

[0088] In still another embodiment, the second reagent 519b may acquire a hemolytic effect by being adjusted to an acidic or low pH instead of being a surfactant. A low pH means a pH lower than that of the first reagent 19u. When the first reagent 519u is neutral or in the range of weak acidity to weak alkalinity, the second reagent 19b is acidic or strongly acidic. When the pH of the first reagent 519u is 6.0 to 8.0, the pH of the second reagent 519b is lower than that, preferably 2.0 to 6.0.

[0089] The second reagent 519b may contain a surfactant and may be adjusted to a low pH.

[0090] In still another embodiment, the second reagent 519b may have a hemolytic effect by being made to have a lower osmotic pressure than the first reagent 19u.

[0091] On the other hand, the first reagent 519u does not contain a surfactant. In another embodiment, the first reagent 519u may contain a surfactant, but the type and concentration of the surfactant must be adjusted so as not to hemolyze red blood cells. Therefore, it is preferable that the first reagent 519u does not contain the same surfactant as the second reagent 519b, or, even if it contains the same surfactant, the surfactant has a lower concentration than the second reagent 519b.

[0092] The third reagent 518u is a staining reagent used for measuring urinary formed elements (red blood cells, white blood cells, epithelial cells, casts, etc.). As the dye contained in the third reagent 518u, a dye that stains membranes is selected in order to stain formed elements that do not have nucleic acids. The third reagent 518u preferably contains an osmotic pressure compensator for the purpose of preventing hemolysis of red blood cells and obtaining stable fluorescence intensity, and is adjusted to 100 to 600 mOsm / kg so as to have an osmotic pressure suitable for classification and measurement. The cell membrane and nucleus (membrane) of urinary formed elements are stained by the third reagent 18u. As a staining reagent containing a dye that stains membranes, a condensed benzene derivative is used, and for example, a cyanine dye can be used. The third reagent 18u is designed to stain not only cell membranes but also nuclear membranes. When the third reagent 518u is used, in nucleated cells such as white blood cells and epithelium, the staining intensity in the cytoplasm (cell membrane) and the staining intensity in the nucleus (nuclear membrane) are combined, resulting in a staining intensity higher than that of urinary formed elements that do not have nucleic acid. This makes it possible to distinguish nucleated cells such as white blood cells and epithelium from urinary formed elements that do not have nucleic acid, such as red blood cells. As the third reagent, the reagent described in U.S. Pat. No. 5,891,733 can be used. U.S. Pat. No. 5,891,733 is incorporated herein by reference. The third reagent 518u is mixed with urine or a body fluid together with the first reagent 519u.

[0093] The fourth reagent 518b is a staining reagent that can accurately measure bacteria even in a specimen containing impurities of the same size as bacteria and fungi. The fourth reagent 518b is described in detail in EP 1136563. A dye that stains nucleic acid is preferably used as the dye contained in the fourth reagent 518b. For example, a cyanine dye described in U.S. Pat. No. 7,309,581 can be used as a staining reagent containing a dye that stains nuclei. The fourth reagent 518b is mixed with urine or a specimen together with the second reagent 519b. EP 1136563 and U.S. Pat. No. 7,309,581 are incorporated herein by reference.

[0094] Therefore, it is preferable that the third reagent 518u contains a dye that stains cell membranes, while the fourth reagent 518b contains a dye that stains nucleic acid. Since urinary formed elements include those that do not have nuclei, such as red blood cells, by making the third reagent 518u contain a dye that stains cell membranes, it is possible to detect urinary formed elements including those that do not have nuclei. In addition, since the second reagent can damage the cell membranes of bacteria, the dye contained in the fourth reagent 518b can efficiently stain the nucleic acids of bacteria and fungi. As a result, bacteria can be measured by a short staining process.

[0095] [3. Waveform data analysis system 1] <Configuration of Waveform Data Analysis System 1> The third embodiment of the present invention relates to a waveform data analysis system. Referring to FIG. 13, the waveform data analysis system according to the third embodiment includes a deep learning device 100A and an analysis device 200A. The vendor-side device 100 operates as the deep learning device 100A, and the user-side device 200 operates as the analysis device 200A. The deep learning device 100A causes the neural network 50 to learn using training data, and provides the deep learning algorithm 60 trained by the training data to the user. The deep learning algorithm 60 consisting of the trained neural network is provided from the deep learning device 100A to the analysis device 200A via a recording medium 98 or a network 99. The analysis device 200A analyzes the waveform data of the cells to be analyzed using the deep learning algorithm 60 consisting of the trained neural network.

[0096] The deep learning device 100A is, for example, a general-purpose computer, and performs deep learning processing based on a flowchart described later. The analysis device 200A is, for example, a general-purpose computer, and performs waveform data analysis processing based on a flowchart described later. The recording medium 98 is, for example, a computer-readable, non-transitory tangible recording medium, such as a DVD-ROM or a USB memory.

[0097] The deep learning device 100A is connected to the measurement unit 400a or the measurement unit 500a. The configuration of the measurement unit 400a or the measurement unit 500a is the same as the measurement unit 400 or the measurement unit 500 described above, respectively. The deep learning device 100A acquires the training waveform data 70 acquired by the measurement unit 400a or the measurement unit 500a. The method of generating the training waveform data 70 is as described above. The analysis device 200A is also connected to the measurement unit 400b or the measurement unit 500b. The configuration of the measurement unit 400b or the measurement unit 500b is the same as the measurement unit 400 or the measurement unit 500 described above, respectively.

[0098] 7 and 11, the measurement unit 400 or the measurement unit 500 includes a flow cell 4113 or 551, respectively. The measurement unit 400 or the measurement unit 500 delivers a biological sample to the flow cell 4113 or 551. The biological sample supplied to the flow cell 4113 or 551 is irradiated with light from the light source 4112 or 553, and forward scattered light, side scattered light, and side fluorescent light emitted from cells in the biological sample are detected by the light detection units 4116, 4121, 4122, 555, 558, and 559. Signals are transmitted from the light detection units 4116, 4121, 4122, 555, 558, and 559 to the vendor side device 100 or the user side device 200. The vendor-side device 100 and the user-side device 200 obtain waveform data from the forward scattered light, side scattered light, and side fluorescent light detected by the light detection units 4116, 4121, 4122, 555, 558, and 559, respectively. <Hardware configuration of deep learning device>

[0099] 14 illustrates an example block diagram of the vendor-side device 100 (deep learning device 100A, deep learning device 100B). The device 10 includes a processing unit 10 (10A, 10B), an input unit 16, and an output unit 17.

[0100] The processing unit 10 includes a CPU (Central Processing Unit) 11 that performs data processing described later, a memory 12 used as a working area for data processing, a storage unit 13 that records a program and processing data described later, a bus 14 that transmits data between each unit, an interface unit 15 that inputs and outputs data to and from an external device, and a GPU (Graphics Processing Unit) 19. The input unit 16 and the output unit 17 are connected to the processing unit 10 via the interface unit 17. Illustratively, the input unit 16 is an input device such as a keyboard or a mouse, and the output unit 17 is a display device such as a liquid crystal display. The GPU 19 functions as an accelerator that assists the arithmetic processing (for example, parallel arithmetic processing) performed by the CPU 11. That is, in the following description, the processing performed by the CPU 11 includes the processing performed by the CPU 11 using the GPU 19 as an accelerator. Here, instead of the GPU 19, a chip that is preferable for neural network calculation may be mounted. Examples of such chips include FPGAs (Field-Programmable Gate Arrays), ASICs (Application specific integrated circuits), Myriad X (Intel), and the like.

[0101] In order to perform the processing of each step described below in Fig. 16, the processing unit 10 pre-records the program according to the present invention and the pre-trained neural network 50 in, for example, an executable format in the storage unit 13. The executable format is, for example, a format generated by conversion from a programming language by a compiler. The processing unit 10 uses the program recorded in the storage unit 13 to perform training processing of the pre-trained neural network 50.

[0102] In the following description, unless otherwise specified, the processing performed by the processing unit 10 means the processing performed by the CPU 11 based on the programs and neural network 50 stored in the storage unit 13 or memory 12. The CPU 11 uses the memory 12 as a working area to temporarily store necessary data (intermediate data during processing, etc.), and records data to be stored for a long time, such as calculation results, in the storage unit 13 as appropriate.

[0103] <Hardware configuration of the analyzer> 15, user side device 200 (analysis device 200A, analysis device 200B, analysis device 200C) includes processing unit 20 (20A, 20B, 20C), input unit 26, and output unit 27.

[0104] The processing unit 20 includes a CPU (Central Processing Unit) 21 that performs data processing described later, a memory 22 used as a working area for data processing, a storage unit 23 that records a program and processing data described later, a bus 24 that transmits data between each unit, an interface unit 25 that inputs and outputs data to and from an external device, and a GPU (Graphics Processing Unit) 29. The input unit 26 and the output unit 27 are connected to the processing unit 20 via the interface unit 25. Illustratively, the input unit 26 is an input device such as a keyboard or a mouse, and the output unit 27 is a display device such as a liquid crystal display. The GPU 29 functions as an accelerator that assists the arithmetic processing (for example, parallel arithmetic processing) performed by the CPU 21. That is, in the following description, the processing performed by the CPU 21 includes the processing performed by the CPU 21 using the GPU 29 as an accelerator.

[0105] Furthermore, in order to perform the processing of each step described in the waveform data analysis process below, the processing unit 20 pre-records the program according to the present invention and a deep learning algorithm 60 of a trained neural network structure in, for example, an executable format in the storage unit 23. The executable format is, for example, a format generated by conversion from a programming language by a compiler. The processing unit 20 performs processing using the program and deep learning algorithm 60 recorded in the storage unit 23.

[0106] In the following description, unless otherwise specified, the processing performed by the processing unit 20 means processing actually performed by the CPU 21 of the processing unit 20 based on the program and deep learning algorithm 60 stored in the storage unit 23 or memory 22. The CPU 21 uses the memory 22 as a working area to temporarily store necessary data (intermediate data during processing, etc.), and records data to be stored for a long time, such as calculation results, in the storage unit 23 as appropriate.

[0107] <Function blocks and processing procedures> (Deep learning processing) 16, a processing unit 10A of a deep learning device 100A according to this embodiment includes a training data generating unit 101, a training data input unit 102, and an algorithm updating unit 103. These functional blocks are realized by installing a program for causing a computer to execute deep learning processing in the storage unit 13 or memory 12 of the processing unit 10A shown in FIG. 14 and executing this program by the CPU 11. A training data database (DB) 104 and an algorithm database (DB) 105 are recorded in the storage unit 13 or memory 12 of the processing unit 10A.

[0108] The training waveform data 70a, 70b, 70c are acquired in advance by the measurement units 400, 500 and stored in advance in the storage unit 13 or the memory 12 of the processing unit 10A. The deep learning algorithm 50 is stored in advance in the algorithm database 105, for example, in association with the type of cell to which the cells to be analyzed belong.

[0109] The processing unit 10A of the deep learning device 100A performs the processing shown in Fig. 17. To explain using each functional block shown in Fig. 16, the processing of steps S11, S14, and S16 shown in Fig. 17 is performed by the training data generation unit 101. The processing of step S12 is performed by the training data input unit 102. The processing of steps S13 and S15 is performed by the algorithm update unit 103. An example of the deep learning process performed by the processing unit 10A will be described with reference to FIG.

[0110] First, the processing unit 10A acquires the training waveform data 70a, 70b, and 70c. The training waveform data 70a is waveform data of forward scattered light, the training waveform data 70b is waveform data of side scattered light, and the training waveform data 70c is waveform data of side fluorescent light. The training waveform data 70a, 70b, and 70c are acquired by the operator's operation by being imported from the measurement units 400 and 500, imported from the recording medium 98, or via the network through the I / F unit 15. When acquiring the training waveform data 70a, 70b, and 70c, information on which type of cell the training waveform data 70a, 70b, and 70c indicate is also acquired. The information on which type of cell the training waveform data 70a, 70b, and 70c indicate may be linked to the training waveform data 70a, 70b, and 70c, or may be input by the operator through the input unit 16.

[0111] In step S11, the processing unit 10A assigns information indicating which of the cell types is linked to the training waveform data 70a, 70b, 70c, label values ​​linked to the cell types stored in the memory 12 or storage unit 13, and label values ​​77 corresponding to the sequence data 72a, 72b, 72c, and sequence data 76a, 76b, 76c obtained by synchronizing the waveform data of the forward scattered light, side scattered light, and side fluorescent light at the time when the waveform data was acquired. In this way, the processing unit 10A generates training data 75.

[0112] 17, the processing unit 10A trains the neural network 50 using the training data 75. The training results of the neural network 50 are accumulated every time training is performed using a plurality of training data 75.

[0113] In the cell type analysis method according to the present embodiment, a convolutional neural network is used, and a stochastic gradient descent method is used, so in step S13, the processing unit 10A judges whether or not training results for a predetermined number of trials have been accumulated. If training results for the predetermined number of trials have been accumulated (YES), the processing unit 10A proceeds to processing in step S14, and if training results for the predetermined number of trials have not been accumulated (NO), the processing unit 10A proceeds to processing in step S15.

[0114] Next, when the training results have been accumulated for a predetermined number of trials, in step S14, the processing unit 10A updates the connection weights w of the neural network 50 using the training results accumulated in step S12. Since the cell type analysis method according to this embodiment uses the stochastic gradient descent method, the connection weights w of the neural network 50 are updated at the stage where the learning results for a predetermined number of trials have been accumulated. The process of updating the connection weights w is specifically a process of performing calculations using the gradient descent method shown in (Equation 11) and (Equation 12) described later.

[0115] In step S15, the processing unit 10A determines whether the neural network 50 has been trained with a prescribed number of training data 75. If training has been performed with a prescribed number of training data 75 (YES), the deep learning process is terminated.

[0116] If the neural network 50 has not been trained with the specified number of training data 75 (NO), the processing unit 10A proceeds from step S15 to step S16, and performs the processes from step S11 to step S15 on the next training waveform data 70.

[0117] According to the process described above, the neural network 50 is trained to obtain the deep learning algorithm 60. (Neural network structure)

[0118] As described above, in this embodiment, a convolutional neural network is used. FIG. 18(a) illustrates the structure of a neural network 50. The neural network 50 includes an input layer 50a, an output layer 50b, and an intermediate layer 50c between the input layer 50a and the output layer 50b, and the intermediate layer 50c is composed of a plurality of layers. The number of layers constituting the intermediate layer 50c can be, for example, 5 layers or more, preferably 50 layers or more, and more preferably 100 layers or more.

[0119] In the neural network 50, multiple nodes 89 are arranged in layers and connected between the layers, so that information propagates in only one direction, as indicated by the arrow D in the figure, from the layer 50a on the input side to the layer 50b on the output side.

[0120] (Operation at each node) FIG. 18(b) is a schematic diagram showing the calculations at each node. Each node 89 receives multiple inputs and calculates one output (z). In the example shown in FIG. 18(b), node 89 receives four inputs. The total input (u) received by node 89 is expressed, for example, by the following (Equation 1). In this embodiment, one-dimensional sequence data is used as training data 75 and analysis data 85, so that when the variables of the calculation formula correspond to two-dimensional matrix data, a process of converting the variables into one dimension is performed.

[0121]

number

[0122] Each input is multiplied by a different weight. In (Equation 1), b is a value called the bias. The output (z) of the node is the output of a given function f for the total input (u) expressed in (Equation 1), and is expressed by the following (Equation 2). The function f is called the activation function.

[0123]

number

[0124] FIG. 18(c) is a schematic diagram showing the operations between nodes. In the neural network 50, nodes that output a result (z) expressed by (Equation 2) for a total input (u) expressed by (Equation 1) are arranged in layers. The output of a node in the previous layer becomes the input of a node in the next layer. In the example shown in FIG. 18(c), the output of node 89a in the left layer becomes the input of node 89b in the right layer. Each node 89b in the right layer receives the output from node 89a in the left layer. A different weight is applied to each connection between each node 89a in the left layer and each node 89b in the right layer. The output of each of the multiple nodes 89a in the left layer is expressed as x 1 ~x 4 Then, the inputs to each of the three nodes 89b in the right layer are expressed by the following (Equation 3-1) to (Equation 3-3).

[0125]

number

[0126]

number

[0127]

number

[0128] (Activation function) In the cell type analysis method according to the embodiment, a rectified linear unit function is used as the activation function. The rectified linear unit function is expressed by the following (Equation 5).

[0129]

number

[0130]

number

[0131] (Neural network training) If the function represented using a neural network is denoted as y(x:w), then the function y(x:w) will change when the parameter w of the neural network is changed. Adjusting the function y(x:w) so that the neural network selects the parameter w that is more suitable for the input x is called neural network learning. Suppose multiple pairs of input and output for the function represented using a neural network are given. If the desired output for a certain input x is d, then the input / output pair can be expressed as {(x 1 , d 1 ), (x 2 , d 2 ), , (x n , d n )}. The set of pairs represented by (x, d) is called training data. Specifically, the set of waveform data (forward scattered light waveform data, side scattered light waveform data, and fluorescent light waveform data) shown in Figure 2 is the training data shown in Figure 2.

[0132] The learning of a neural network is based on what kind of input / output pairs (x n , d n ), input x n Given the neural network output y(xn :w) but the output is d n This means adjusting the weights w so that the function is as close as possible to the training data. The error function is the closeness of the function expressed by the neural network to the training data.

[0133]

number

[0134]

number

[0135]

number

[0136]

number

[0137] The target output d by the softmax function of (Eq. 7) n Let d be 1 only if the output is the correct class, and 0 otherwise. Let d be the target output. n =[d n1 , , d nK ], for example, input x n The correct class is C 3 If so, the target output d n3 Only the input is 1, and the other target outputs are 0. When encoded in this way, the posterior distribution is expressed by the following (Equation 9).

[0138]

number

[0139]

number

[0140] Minimizing the error function E(w) with respect to the parameter w is the same as finding a local minimum point of the function E(w). The parameter w is the weight of the connection between nodes. The minimum point of the weight w is found by iterative calculations that start with an arbitrary initial value and repeatedly update the parameter w. One example of such calculations is the gradient descent method.

[0141] In the gradient descent method, a vector expressed by the following (Equation 11) is used.

number

[0142]

number

number

[0143] The calculation by (Equation 12) may be performed on all training data (n=1, . . . , N) or only on a part of the training data. A gradient descent method performed on only a part of the training data is called a stochastic gradient descent method. In the cell type analysis method according to the embodiment, the stochastic gradient descent method is used.

[0144] (Waveform data analysis processing) FIG. 19 shows a functional block diagram of an analysis device 200A that performs waveform data analysis processing from analysis waveform data 80a, 80b, and 80c to generating an analysis result 83. The processing unit 20A of the analysis device 200A includes an analysis data generating unit 201, an analysis data input unit 202, and an analysis unit 203. These functional blocks are realized by installing a program for causing a computer according to the present invention to execute waveform data analysis processing in the storage unit 23 or memory 22 of the processing unit 20A shown in FIG. 15, and executing this program by the CPU 21. The training data stored in the training data database (DB) 104 and the trained deep learning algorithm 60 stored in the algorithm database (DB) 105 are provided from the deep learning device 100A via a recording medium 98 or a network 99, and are recorded in the storage unit 23 or memory 22 of the processing unit 20A.

[0145] The analytical waveform data 80a, 80b, 80c are acquired by the measurement units 400, 500 and stored in the storage unit 23 or memory 22 of the processing unit 20A. The trained deep learning algorithm 60 including the trained connection weights w is stored in the algorithm database 105, for example, in association with the cell type to which the cell to be analyzed belongs, and functions as a program module that is part of a program that causes a computer to execute a waveform data analysis process. That is, the deep learning algorithm 60 is used in a computer having a CPU and memory, and is used to calculate the probability that the cell to be analyzed belongs to any one of the cell types and generate an analysis result 83 regarding the cell.

[0146] The generated analysis result 83 is output as follows. The CPU 21 of the processing unit 20A causes the computer to function so as to execute calculations or processing of specific information according to the intended use. Specifically, the CPU 21 of the processing unit 20A generates the analysis result 83 relating to cells using the deep learning algorithm 60 recorded in the storage unit 23 or the memory 22. The CPU 21 of the processing unit 20A inputs the analysis data 85 to the input layer 60a, and outputs the label value relating to the type of cell to which the cell to be analyzed belongs from the output layer 61, that is, the label value of the type of cell to which the cell included in the analysis waveform data is identified to belong.

[0147] 20, the process of step S21 is performed by the analysis data generation unit 201. The processes of steps S22, S23, S24, and S26 are performed by the analysis data input unit 202. The process of step S25 is performed by the analysis unit 203.

[0148] An example of waveform data analysis processing performed by the processing unit 20A from the analytical waveform data 80a, 80b, and 80c to generating an analysis result 83 regarding a cell will be described with reference to FIG.

[0149] First, the processing unit 20A acquires the analysis waveform data 80a, 80b, and 80c. The analysis waveform data 80a, 80b, and 80c are acquired by a user operation or automatically, by being imported from the measurement units 400 and 500, by being imported from the recording medium 98, or by being acquired via the I / F unit 25 via the network.

[0150] In step S21, the processing unit 20A generates the analysis result 83 on the cell from the sequence 82a, 82b, 82c according to the procedure explained in the above-mentioned method for generating the analysis data.

[0151] Next, in step S22, the processing unit 20A acquires a deep learning algorithm stored in the algorithm database 105. Note that the order of steps S21 and S22 does not matter.

[0152] Next, in step S23, the processing unit 20A inputs the analysis result 83 regarding the cell into the deep learning algorithm. The processing unit 20A outputs the label value determined to belong to the analysis target cell from which the analysis waveform data 80a, 80b, and 80c are obtained from the deep learning algorithm according to the procedure described in the above-mentioned waveform data analysis method. The processing unit 20A stores this label value in the memory 22 or the storage unit 23.

[0153] In step S24, the processing unit 20A judges whether or not all of the initially acquired analytical waveform data 80a, 80b, and 80c have been identified. If identification of all of the analytical waveform data 80a, 80b, and 80c has been completed (YES), the processing unit 20A proceeds to step S25, where the analysis results including the cell-related information 83 are output. If identification of all of the analytical waveform data 80a, 80b, and 80c has not been completed (NO), the processing unit 20A proceeds to step S26, where the processing of steps S22 to S24 is performed on the analytical waveform data 80a, 80b, and 80c that have not yet been identified.

[0154] According to this embodiment, it becomes possible to identify the type of cells regardless of the skill of the examiner.

[0155] <Computer Program> This embodiment includes a computer program for analyzing waveform data to analyze the types of cells, which causes a computer to execute the processes of steps S11 to S16 and / or S21 to S26.

[0156] Furthermore, some embodiments of the present invention relate to a program product, such as a storage medium, that stores a computer program. That is, the computer program is stored in a storage medium, such as a hard disk, a semiconductor memory element such as a flash memory, or an optical disk. The format of the program stored in the storage medium is not limited as long as the vendor-side device 100 and / or the user-side device 200 can read the program. It is preferable that the recording in the storage medium is non-volatile.

[0157] [4. Waveform Data Analysis System 2] <Configuration of Waveform Data Analysis System 2> Another aspect of a waveform data analysis system is described. FIG. 21 shows a configuration example of the second waveform data analysis system. The second waveform data analysis system includes a user-side device 200, which operates as an integrated analysis device 200B. The analysis device 200B is, for example, a general-purpose computer, and performs both the deep learning process and the waveform data analysis process described in the above waveform data analysis system 1. In other words, the second waveform data analysis system is a stand-alone system that performs deep learning and waveform data analysis on the user side. In the second waveform data analysis system, the integrated analysis device 200B installed on the user side performs the functions of both the deep learning device 100A and the analysis device 200A according to this embodiment.

[0158] In Fig. 21, the analysis device 200B is connected to the measurement units 400b and 500b. The measurement unit 400 illustrated in Fig. 5(a) and the measurement unit 500 illustrated in Fig. 5(b) acquire training waveform data 70a, 70b, and 70c during deep learning processing, and acquire analysis waveform data 80a, 80b, and 80c during waveform data analysis processing.

[0159] <Hardware configuration> The hardware configuration of the analysis device 200B is similar to the hardware configuration of the user side device 200 shown in FIG.

[0160] <Function blocks and processing procedures> FIG. 22 shows a functional block diagram of the analysis device 200B. The processing unit 20B of the analysis device 200B includes a training data generating unit 101, a training data input unit 102, an algorithm updating unit 103, an analysis data generating unit 201, an analysis data input unit 202, an analysis unit 203, and an analysis result 83 related to the type of cell. These functional blocks are realized by installing a program for causing a computer to execute deep learning processing and waveform data analysis processing in the storage unit 23 or memory 22 of the processing unit 20B, as exemplified in FIG. 15, and executing this program by the CPU 21. The training data database (DB) 104 and the algorithm database (DB) 105 are stored in the storage unit 23 or memory 22 of the processing unit 20B, and both are used in common during deep learning and waveform data analysis processing. The deep learning algorithm 60 including the trained neural network is stored in advance in the algorithm database 105 in association with, for example, the type of cell to which the cell to be analyzed belongs or the type of cell. The connection weights w are updated by the deep learning process, and are stored in the algorithm database 105 as a new deep learning algorithm 60. The training waveform data 70a, 70b, 70c are assumed to be acquired in advance by the measurement units 400b, 500b as described above, and to be stored in advance in the training data database (DB) 104 or the storage unit 23 or memory 22 of the processing unit 20B. The analysis waveform data 80a, 80b, 80c of the sample to be analyzed are assumed to be acquired in advance by the measurement units 400b, 500b, and to be stored in advance in the storage unit 23 or memory 22 of the processing unit 20B.

[0161] The processing unit 20B of the analysis device 200B performs the process shown in FIG. 17 during deep learning processing, and performs the process shown in FIG. 20 during waveform data analysis processing. Explaining using the functional blocks shown in FIG. 22, during deep learning processing, the processes of steps S11, S15, and S16 are performed by the training data generation unit 101. The process of step S12 is performed by the training data input unit 102. The processes of steps S13 and S18 are performed by the algorithm update unit 103. During waveform data analysis processing, the process of step S21 is performed by the analysis data generation unit 201. The processes of steps S22, S23, S24, and S26 are performed by the analysis data input unit 202. The process of step S25 is performed by the analysis unit 203.

[0162] The procedure of the deep learning process and the procedure of the waveform data analysis process performed by the analysis device 200B are similar to the procedures performed by the deep learning device 100A and the analysis device 200A, respectively. However, the analysis device 200B acquires the training waveform data 70a, 70b, and 70c from the measurement units 400b and 500b.

[0163] In the analysis device 200B, the user can check the discrimination accuracy of the trained deep learning algorithm 60. In the unlikely event that the judgment result of the deep learning algorithm 60 differs from the judgment result based on the user's observation of the waveform data, the deep learning algorithm can be retrained using the analysis waveform data 80a, 80b, 80c as the training data 70a, 70b, 70c and the judgment result based on the user's observation of the waveform data as the label value 77. In this way, the training efficiency of the deep learning algorithm 50 can be further improved.

[0164] [5. Waveform Data Analysis System 3] <Configuration of Waveform Data Analysis System 3> Another aspect of a waveform data analysis system is described. FIG. 23 shows an example of the configuration of the third waveform data analysis system. The third waveform data analysis system includes a vendor-side device 100 and a user-side device 200. The vendor-side device 100 operates as an integrated analysis device 100B, and the user-side device 200 operates as a terminal device 200C. The analysis device 100B is, for example, a general-purpose computer, and is a cloud server-side device that performs both the deep learning process and the waveform data analysis process described in the waveform data analysis system 1. The terminal device 200C is, for example, a general-purpose computer, and is a user-side terminal device that transmits analysis waveform data 80a, 80b, and 80c of the cells to be analyzed to the analysis device 100B through a network 99, and receives the analysis result 83 from the analysis device 100B through the network 99.

[0165] In the third waveform data analysis system, an integrated analysis device 100B installed on the vendor side performs the functions of both the deep learning device 100A and the analysis device 200A. On the other hand, the third waveform data analysis system includes a terminal device 200C, and provides an input interface for analysis waveform data 80a, 80b, and 80c and an output interface for analysis result waveform data to the user-side terminal device 200C. In other words, the third waveform data analysis system is a cloud service type system in which the vendor side performing deep learning processing and waveform data analysis processing includes an input interface for providing analysis waveform data 80a, 80b, and 80c to the user side, and an output interface for providing information 83 related to cells to the user side. The input interface and the output interface may be integrated.

[0166] Analysis device 100B is connected to measurement units 400a, 500a, and acquires training waveform data 70a, 70b, 70c acquired by measurement units 400a, 500a.

[0167] The terminal device 200C is connected to the measurement units 400b and 500b, and acquires analysis waveform data 80a, 80b, and 80c acquired by the measurement units 400b and 500b.

[0168] <Hardware configuration> The hardware configuration of the analysis device 100B is similar to that of the vendor-side device 100 shown in Fig. 14. The hardware configuration of the terminal device 200C is similar to that of the user-side device 200 shown in Fig. 15. <Function blocks and processing procedures> FIG. 24 shows a functional block diagram of the analysis device 100B. The processing unit 10B of the analysis device 100B includes a training data generating unit 101, a training data input unit 102, an algorithm updating unit 103, an analysis data generating unit 201, an analysis data input unit 202, and an analysis unit 203. These functional blocks are realized by installing a program that causes a computer to execute deep learning processing and waveform data analysis processing in the storage unit 13 or memory 12 of the processing unit 10B shown in FIG. 14, and executing this program by the CPU 11. A training data database (DB) 104 and an algorithm database (DB) 105 are stored in the storage unit 13 or memory 12 of the processing unit 10B, and both are used in common during deep learning and waveform data analysis processing. The neural network 50 is pre-stored in the algorithm database 105, for example in association with the type or species of cell to which the cell being analyzed belongs, and the connection weights w are updated by deep learning processing and stored in the algorithm database 105 as a deep learning algorithm 60.

[0169] The training waveform data 70a, 70b, 70c are acquired in advance by the measurement units 400a, 500a as described above, and are stored in advance in the training data database (DB) 104 or the storage unit 13 or memory 12 of the processing unit 10B. The analysis waveform data 80a, 80b, 80c are acquired by the measurement units 400b, 500b, and are recorded in advance in the storage unit 23 or memory 22 of the processing unit 20C of the terminal device 200C.

[0170] The processing unit 10B of the analysis device 100B performs the process shown in FIG. 17 during deep learning processing, and performs the process shown in FIG. 20 during waveform data analysis processing. Explaining using the functional blocks shown in FIG. 24, during deep learning processing, the processes of steps S11, S15, and S16 are performed by the training data generation unit 101. The process of step S12 is performed by the training data input unit 102. The processes of steps S13 and S18 are performed by the algorithm update unit 103. During waveform data analysis processing, the process of step S21 is performed by the analysis data generation unit 201. The processes of steps S22, S23, S24, and S26 are performed by the analysis data input unit 202. The process of step S25 is performed by the analysis unit 203.

[0171] The deep learning process procedure and the waveform data analysis process procedure performed by the analysis device 100B are similar to the procedures performed by the deep learning device 100A and the analysis device 200A according to this embodiment, respectively.

[0172] The processing unit 10B receives the training waveform data 70a, 70b, and 70c from the user terminal device 200C, and generates training data 75 in accordance with steps S11 to S16 shown in FIG.

[0173] 20, the processing unit 10B transmits the analysis result including the cell-related information 83 to the user-side terminal device 200C. In the user-side terminal device 200C, the processing unit 20C outputs the received analysis result to the output unit 27.

[0174] As described above, the user of the terminal device 200C can obtain the analysis result 83 relating to the type of cell as the analysis result by transmitting the analytical waveform data 80a, 80b, 80c to the analysis device 100B.

[0175] According to the analysis device 100B according to the third embodiment, a user can use the classifier without acquiring the training data database 104 and the algorithm database 105 from the deep learning device 100A. This makes it possible to provide a service for identifying cell types as a cloud service.

[0176] [6. Other forms] Although the present invention has been described above with reference to the outline and specific embodiments, the present invention is not limited to the outline and each embodiment described above.

[0177] In each scene image analysis system, the processing units 10A and 10B are realized as an integrated device, but the processing units 10A and 10B do not have to be an integrated device, and the CPU 11, memory 12, storage unit 13, GPU 19, etc. may be located in different places and connected by a network. The processing units 10A and 10B, the input unit 16, and the output unit 17 do not necessarily have to be located in one place, and may be located in different places and connected to each other so as to be able to communicate with each other by a network. The processing units 20A, 20B, and 20C are similar to the processing units 10A and 10B.

[0178] In the above first to third embodiments, each of the functional blocks of the training data generation unit 101, the training data input unit 102, the algorithm update unit 103, the analysis data generation unit 201, the analysis data input unit 202, and the analysis unit 203 is executed by a single CPU 11 or a single CPU 21, but each of these functional blocks does not necessarily have to be executed by a single CPU, and may be executed in a distributed manner by multiple CPUs. Also, each of these functional blocks may be executed in a distributed manner by multiple GPUs, or may be executed in a distributed manner by multiple CPUs and multiple GPUs.

[0179] In the second and third embodiments, a program for performing the processing of each step described in Fig. 17 and Fig. 20 is pre-recorded in the storage units 13 and 23. Alternatively, the program may be installed in the processing units 10B and 20B from a computer-readable, non-transitory, tangible recording medium 98, such as a DVD-ROM or a USB memory. Alternatively, the processing units 10B and 20B may be connected to a network 99, and the program may be downloaded from, for example, an external server (not shown) via the network 99 and installed.

[0180] In each waveform data analysis system, the input units 16, 26 are input devices such as a keyboard or a mouse, and the output units 17, 27 are realized as display devices such as a liquid crystal display. Alternatively, the input units 16, 26 and the output units 17, 27 may be integrated into one unit and realized as a touch panel type display device. Alternatively, the output units 17, 27 may be configured as a printer or the like.

[0181] In each of the above waveform data analysis systems, the measurement units 400a, 500a are directly connected to the deep learning device 100A or the analysis device 100B, but the flow cytometer 300 may be connected to the deep learning device 100A or the analysis device 100B via a network 99. Similarly, the flow cytometer 400 is directly connected to the analysis device 200A or the analysis device 200B, but the measurement units 400b, 500b may be connected to the analysis device 200A or the analysis device 200B via a network 99.

[0182] FIG. 25 shows an embodiment of the analysis result output to the output unit 27. FIG. 25 shows the types of cells to which the label values ​​shown in FIG. 3 are attached, and the number of cells of each type of cell, contained in the biological sample measured by flow cytometry. Instead of displaying the number of cells, or together with displaying the number of cells, the ratio (for example, %) of each cell type in the total number of counted cells may be output. The count of the number of cells can be calculated by multiplying the number of label values ​​(the number of the same label value) corresponding to each outputted cell type. In addition, the output result may include a warning indicating that abnormal cells are contained in the biological sample. FIG. 25 shows an example in which an exclamation mark is added to the abnormal cell item as a warning, but this is not limited to this. Furthermore, the distribution of each cell tumor may be output as a scattergram. When outputting as a scattergram, for example, the highest value when the signal intensity is acquired can be plotted with, for example, the side fluorescent intensity as the vertical axis and the side scattered light intensity as the horizontal axis. EXAMPLES

[0183] 1. Building a deep learning model Blood samples taken from healthy subjects were measured as healthy blood samples, and XN CHECK Lv2 (Streck's control blood (fixed and processed)) was measured as unhealthy blood samples using a Sysmex XN-1000. Fluorocell WDF manufactured by Sysmex Corporation was used as the fluorescent staining reagent. Lysercell WDF manufactured by Sysmex Corporation was used as the hemolytic agent. For each cell contained in each specimen, waveform data of forward scattered light, side scattered light, and side fluorescent light were obtained at 10 nanosecond intervals from the start of forward scattered light measurement for 1024 points. For the healthy blood samples, waveform data of cells in blood taken from eight healthy subjects was pooled as digital data. The waveform data of each cell was manually classified into neutrophils (NEUT), lymphocytes (LYMPH), monocytes (MONO), eosinophils (EO), basophils (BASO), and immature granulocytes (IG), and each waveform data was annotated (labeled) by cell type. The measurement start point was the point when the signal strength of the forward scattered light exceeded the threshold, and the acquisition points of the waveform data of the forward scattered light, side scattered light, and side fluorescent light were synchronized to generate training data. The control blood was also annotated as cells derived from control blood (CONT). The training data was input into the deep learning algorithm and allowed to learn.

[0184] Analytical waveform data was obtained using the Sysmex XN-1000 in the same manner as the training data for blood cells from healthy individuals other than the learned cell data. Analytical data was created by mixing waveform data from control blood. In this analytical data, blood cells from healthy individuals and blood cells from control blood overlapped on the scattergram, making it impossible to distinguish them at all using conventional methods. This analytical data was input into the deep learning algorithm that was constructed, and data on the type of each individual cell was obtained.

[0185] The results are shown as a confusion matrix in Figure 26. The horizontal axis shows the judgment results by the constructed deep learning algorithm, and the vertical axis shows the judgment results by humans (reference method). Although there was some confusion between basophils and lymphocytes, and between basophils and ghosts, the judgment results by the constructed deep learning algorithm showed a 98.8% agreement rate with the judgment results by the reference method.

[0186] Next, ROC analysis was performed for each cell type to evaluate the sensitivity and specificity. Figure 27(a) shows the ROC curves for neutrophils, Figure 27(b) shows the ROC curves for lymphocytes, Figure 27(c) shows the ROC curves for monocytes, Figure 28(a) shows the ROC curves for neutrophils, Figure 28(b) shows the ROC curves for basophils, and Figure 28(c) shows the ROC curves for control blood (CONT). The sensitivity and specificity were 99.5% and 99.6% for neutrophils, 99.4% and 99.5% for lymphocytes, 98.5% and 99.9% for monocytes, 97.9% and 99.8% for eosinophils, 71.0% and 81.4% for basophils, and 99.8% and 99.6% for control blood (CONT), all of which showed good results.

[0187] From the above results, it became clear that it is possible to determine the type of cell by using a deep learning algorithm based on signals obtained from cells contained in a biological sample based on waveform data.

[0188] Furthermore, when unhealthy blood cells such as those in control blood are mixed with healthy blood cells, it is sometimes difficult to determine the presence or absence of such cells using the conventional scattergram method. However, it has been shown that the deep learning algorithm of the present embodiment can determine the presence or absence of unhealthy blood cells even when the unhealthy blood cells are mixed with healthy blood cells. [Explanation of symbols]

[0189] 4000, 4000' cell analyzer 10 Processing section 50 Pre-trained Deep Learning Algorithms 50a Input layer 50b output layer 60 Pre-Trained Deep Learning Algorithms 75 training data 80 Analysis Data 83 Cell type analysis results

Claims

1. A cell analysis device for analyzing cells contained in a blood sample, comprising: a sample preparation unit that prepares a first measurement sample and a second measurement sample from the blood sample; an electrical resistance detection unit that causes the first measurement sample to flow through an opening to which a voltage is applied, and acquires a first signal that reflects a change in electrical resistance caused by the cells contained in the first measurement sample passing through the opening; an optical detection unit including a light source, a condensing lens, a flow cell, and a detector, which flows the second measurement sample through the flow cell, condenses light emitted from the light source by the condensing lens, irradiates the cells contained in the second measurement sample flowing through the flow cell, and obtains a second signal related to each of the cells passing a predetermined position in the flow cell by the detector; A processing unit, The processing unit includes: Counting at least red blood cells among the cells based on the first signal; inputting numerical data corresponding to the second signal into a classifier having a neural network structure; determining the type of the cells contained in the second measurement sample for each cell based on the result output from the classifier; Depending on the result of the determination, information regarding the abnormal cells is output. the second signal is acquired while the cell passes through the predetermined location within the flow cell; The numerical data includes a plurality of values ​​corresponding to the second signal at a plurality of time points while the cell passes through the predetermined position. Cell analysis device.

2. The abnormal cell is a nucleated red blood cell, an erythroblast, a lymphoblast, a plasma cell, an atypical lymphocyte, a reactive lymphocyte, a proerythroblast, a basophilic erythroblast, a polychromatic erythroblast, a normochromatic erythroblast, a promegaloblast, a basophilic megaloblast, a polychromatic megaloblast, a normochromatic megaloblast, a megakaryocyte, or a tumor cell; The cell analysis device according to claim 1 .

3. The optical detection unit includes: A first detector and a second detector, acquiring a first second signal by the first detector; acquiring a second second signal by the second detector; The processing unit includes: inputting the first numerical data corresponding to the first second signal for the individual cell and the second numerical data corresponding to the second second signal for the individual cell into the classifier; the first second signal and the second second signal are acquired while the cell passes through the predetermined position within the flow cell; the first numerical data includes a plurality of values ​​corresponding to the first second signal at a plurality of time points while the cell passes through the predetermined position, The second numerical data includes a plurality of values ​​corresponding to the second second signal at a plurality of time points while the cell passes through the predetermined position. The cell analysis device according to claim 1 .

4. The plurality of time points in the first second signal and the plurality of time points in the second second signal are simultaneous with each other. The cell analysis device according to claim 3 .

5. The processing unit outputs the number of cells for each type based on the type of each cell determined based on the result output from the classifier. The cell analysis device according to claim 1 .

6. The processing unit includes a CPU and an accelerator that assists the arithmetic processing performed by the CPU. The cell analysis device according to claim 1 .

7. A cell analysis method using a classifier having a neural network structure in a cell analyzer for analyzing cells contained in a blood sample, comprising: A first measurement sample prepared from the blood sample is passed through the opening to which the voltage is applied; counting at least red blood cells among the cells based on a change in electrical resistance caused by the cells passing through the opening; A second measurement sample prepared from the blood sample is passed through a flow cell; The light emitted from the light source is condensed by a condenser lens, and the light is irradiated onto the cells contained in the second measurement sample flowing through the flow cell; acquiring a signal relating to each of the cells irradiated with the light, and inputting numerical data corresponding to the acquired signal relating to each of the cells into the classifier; determining the type of each of the cells based on the result output from the classifier; Depending on the result of the determination, information regarding the abnormal cells is output. The signal is acquired while the cell passes through a predetermined location within the flow cell; The numerical data includes a plurality of values ​​corresponding to the signal at a plurality of time points while the cell passes through the predetermined position. Cell analysis method.

8. The abnormal cell is a nucleated red blood cell, an erythroblast, a lymphoblast, a plasma cell, an atypical lymphocyte, a reactive lymphocyte, a proerythroblast, a basophilic erythroblast, a polychromatic erythroblast, a normochromatic erythroblast, a promegaloblast, a basophilic megaloblast, a polychromatic megaloblast, a normochromatic megaloblast, a megakaryocyte, or a tumor cell; The cell analysis method according to claim 7.

9. In acquiring a signal relating to an individual cell illuminated with the focused light from the light source, a first signal and a second signal are acquired; In inputting the numerical data to the classifier, the first numerical data corresponding to the first signal and the second numerical data corresponding to the second signal are input to the classifier; the first signal and the second signal are acquired while the cell passes through the predetermined position within the flow cell; the first numerical data includes a plurality of values ​​corresponding to the first signal at a plurality of time points while the cell passes through the predetermined position; The second numerical data includes a plurality of values ​​corresponding to the second signal at a plurality of time points while the cell passes through the predetermined position. The cell analysis method according to claim 7.

10. The plurality of time points in the first signal and the plurality of time points in the second signal are simultaneous. The cell analysis method according to claim 9.

11. and outputting the number of cells for each type based on the type of each cell determined based on the result output from the classifier. The cell analysis method according to claim 7.

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