CN114419620A - Abnormal sample identification method and device, sample analyzer and storage medium - Google Patents
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Abstract
The application discloses an abnormal sample identification method and device, a sample analyzer and a storage medium. The method comprises the following steps: obtaining a white blood cell scatter diagram; processing the white blood cell scatter diagram to obtain a classification result, wherein the classification result is used for representing the cell type in the white blood cell scatter diagram; analyzing based on the cell type to obtain an identification result, wherein the identification result comprises abnormal cell information in the white blood cell scatter diagram; and outputting prompt information according to the abnormal cell information, so that the abnormal sample can be identified based on the classification result of the white blood cells, the abnormal sample identification rate is improved, and an alarm is effectively given.
Description
Technical Field
The invention relates to the technical field of medical detection and analysis, in particular to an abnormal sample identification method and device, a sample analyzer and a storage medium.
Background
In a medical or experimental scenario, various samples are often required to be tested and analyzed. For example, a blood cell analyzer is generally used to count and classify red blood cells, platelets, and white blood cells in blood, and when some abnormal cells exist in the white blood cells, an abnormal alarm may be given.
The identification rate of abnormal samples is low in the conventional method, abnormal cells cannot be effectively distinguished from normal cells, the number of the abnormal cells cannot be effectively identified, and effective alarm prompt cannot be performed. In addition, the scatter diagram of the abnormal sample may present various forms, and generally, for a sample with a plurality of abnormal cells, the various samples are overlapped with each other and are difficult to distinguish, so that the number of the abnormal cells cannot be accurately obtained, and the identification accuracy of the abnormal sample is low.
Disclosure of Invention
The application provides an abnormal sample identification method and device, a sample analyzer and a storage medium.
In a first aspect, a method for identifying an abnormal sample is provided, which includes:
obtaining a white blood cell scatter diagram;
processing the white blood cell scatter diagram to obtain a classification result, wherein the classification result is used for representing the cell type in the white blood cell scatter diagram;
analyzing based on the cell type to obtain an identification result, wherein the identification result comprises abnormal cell information in the white blood cell scatter diagram;
and outputting prompt information according to the abnormal cell information.
In an alternative embodiment, the cell type comprises a neutrophil;
said analyzing based on said cell type to obtain an identification result comprising:
calculating morphological parameters of the neutrophils;
comparing the morphological parameter of the neutrophils to a preset neutrophil parameter threshold to determine if immature granulocytes are present in the neutrophils.
In an alternative embodiment, the morphological parameters include mean and variance at different angles;
the comparing the morphological parameter of the neutrophil to a preset neutrophil parameter threshold to determine whether an immature granulocyte is present in the neutrophil, comprising:
and if the mean value and the variance corresponding to any neutrophil are respectively greater than the mean value and the variance in the preset neutrophil parameter threshold, determining the neutrophil as the immature granulocyte.
In an alternative embodiment, the cell type comprises a lymphocyte;
said analyzing based on said cell type to obtain an identification result comprising:
calculating morphological parameters of the lymphocytes;
and comparing the morphological parameters of the lymphocytes with a preset lymphocyte parameter threshold value to determine whether abnormal lymphocytes exist.
In an alternative embodiment, said analyzing based on said cell type to obtain an identification result comprises:
obtaining the number of overlapping particles of monocytes and said neutrophils;
comparing the number of overlapping particles to a first number threshold to determine whether there is immature granulocytes or nucleus left-shift.
In an optional embodiment, the method further comprises:
obtaining the sum of the ratios of the monocytes and the neutrophils;
said comparing said number of overlapping particles to a first number threshold to determine whether there is a premature granulocyte or nucleus left shift, comprising:
comparing the size relationship of the number of overlapping particles to a second number threshold, and the size relationship of the sum of the ratios to a ratio threshold to determine whether the immature granulocytes are present.
In an alternative embodiment, the abnormal cell information includes: the proportion and number of first abnormal cells;
the outputting of the prompt information according to the abnormal cell information includes:
if the proportion and the number of the first abnormal cells meet the abnormal prompt condition, outputting first prompt information; or,
and determining whether to output the first prompt message according to the proportion and the number of the first abnormal cells and the proportion and the number of the at least one second abnormal cell.
In a second aspect, an abnormal sample identification apparatus is provided, including:
the acquisition module is used for acquiring a white blood cell scatter diagram;
the classification processing module is used for processing the white blood cell scatter diagram to obtain a classification result, and the classification result is used for representing the cell type in the white blood cell scatter diagram;
the identification module is used for analyzing based on the cell type to obtain an identification result, and the identification result comprises abnormal cell information in the white blood cell scatter diagram;
and the prompting module is used for outputting prompting information according to the abnormal cell information.
In a third aspect, there is provided a sample analyzer comprising an abnormal sample identification device as described in the second aspect.
In a fourth aspect, there is provided a computer storage medium storing one or more instructions adapted to be loaded by a processor and to perform the steps of the first aspect and any possible implementation thereof.
According to the abnormal sample identification method, a white blood cell scatter diagram is obtained; processing the white blood cell scatter diagram to obtain a classification result, wherein the classification result is used for representing the cell type in the white blood cell scatter diagram; analyzing based on the cell type to obtain an identification result, wherein the identification result comprises abnormal cell information in the white blood cell scatter diagram; and outputting prompt information according to the abnormal cell information, so that the abnormal sample can be identified based on the classification result of the white blood cells, the abnormal sample identification rate is improved, and an alarm is effectively given.
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In order to more clearly illustrate the technical solutions in the embodiments or the background art of the present application, the drawings required to be used in the embodiments or the background art of the present application will be described below.
Fig. 1 is a schematic flowchart of an abnormal sample identification method according to an embodiment of the present disclosure;
FIG. 2 is a schematic flow chart of a specific cell analysis method provided in an embodiment of the present application;
FIG. 3 is a sample of immature cells and nuclei left shifted according to the present invention;
fig. 4 is a schematic structural diagram of an abnormal sample identification device according to an embodiment of the present application.
Detailed Description
In order to make the technical solutions of the present application better understood, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The terms "first," "second," and the like in the description and claims of the present application and in the above-described drawings are used for distinguishing between different objects and not for describing a particular order. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.
In the field of blood detection, the cell types of blood cells are accurately identified, abnormal cells are identified, and the method is favorable for knowing the relevant physiological parameters of blood. However, the detection of abnormal cells does not directly lead to the conclusion of disease, and whether the abnormal cells are abnormal depends on the number of abnormal cells, abnormal morphology, other references related to physiological parameters, and the like. Therefore, the abnormal sample identification method described in the embodiments of the present application does not belong to the diagnosis and treatment method of diseases.
The embodiments of the present application will be described below with reference to the drawings.
Referring to fig. 1, fig. 1 is a schematic flowchart illustrating an abnormal sample identification method according to an embodiment of the present disclosure. The method can comprise the following steps:
101. and obtaining a white blood cell scatter diagram.
The subject of the present application may be an abnormal sample identification device, and in a specific application, may be a sample analyzer, such as a blood cell analyzer.
In the embodiment of the present application, a white blood cell scattergram generated after an analyzer processes any sample may be obtained, and step 102 is executed.
The scatter diagram is a distribution pattern in which a plurality of coordinate points are formed by two sets of data, and the distribution of the coordinate points is examined to determine whether or not there is any correlation between two variables or to summarize the coordinate points. Scatter plots show the sequences as a set of points. The values are represented by the positions of the points in the graph. Categories are represented by different labels in the chart. Scatter plots are typically used to compare aggregated data across categories.
In the embodiment of the application, the blood sample is subjected to chemical staining after being diluted by suction, blood cells are arranged in a line under the coating of a reagent in sheath flow and pass through the detection small holes, and a semiconductor laser beam irradiates on the blood cells. When blood cells pass through the laser channel, light beams generate light scattering in different directions of each blood cell, light signals are converted into electric pulses by detecting the scattered light, information about the size and the material of the cells can be obtained, and a two-dimensional scatter diagram is drawn.
102. And processing the white blood cell scatter diagram to obtain a classification result, wherein the classification result is used for representing the cell type in the white blood cell scatter diagram.
In the embodiment of the present application, the white blood cells can be classified into five types using the side scattered light SSC and the forward scattered light FSC: neutrophils, monocytes, lymphocytes, eosinophils, basophils, and five cell types from the above classification results. Wherein, the abscissa of the scatter diagram: the SSC axis, representing the signal intensity of the side scattered light; ordinate: the FSC axis, represents the signal intensity of the forward scattered light.
Optionally, the cells may be classified according to other methods, and the cells may be classified according to different dimensions, which is not limited in the embodiments of the present application.
Specifically, the step 102 may include:
matching the white blood cell scatter diagram with a preset template, wherein the preset template can be a cell classification rule template preset based on experience; through template matching, various cells can be identified, the number of various abnormal cells can be obtained, and the proportion of various abnormal cells can be further counted. Wherein, the proportion of abnormal cells refers to the ratio of the number of abnormal cells to the number of total cells. The embodiment of the application does not limit the specific content of the preset template.
The abnormal cells referred to in the examples of the present application refer to cells that should not be present in the blood of normal persons, such as immature granulocytes, which are relatively large in size and are usually distributed in the upper right corner of the scattergram.
In an alternative embodiment, the abnormal cell comprises: nucleated red blood cells, abnormal lymphocytes, immature granulocytes, and the like.
103. And analyzing based on the cell types to obtain a recognition result, wherein the recognition result comprises abnormal cell information in the white blood cell scatter diagram.
In the embodiment of the present application, the abnormal cell information may be determined according to the identified cells, and the abnormal cell information may mainly include the proportion and the number of the abnormal cells, and then the corresponding alarm may be performed based on the relation between the proportion and the number of the abnormal cells, that is, step 104 is performed.
Specifically, the percentage of normal cells (including lymphocytes, monocytes, etc.) adjacent to the abnormal cells can be determined to determine whether the corresponding abnormal cells exist. For example, for immature granulocytes, judging whether immature granulocytes exist according to the proportion of monocytes or neutrophils, if the required proportion is lower than a preset proportion value, judging that the immature granulocytes exist, and setting the preset proportion value of different types of cells according to requirements; similar methods are used for other abnormal cells, which are not limited in the examples of the present application.
In an alternative embodiment, the cell types include neutrophils;
the step 103 includes:
31. calculating morphological parameters of the neutrophils;
32. comparing the morphological parameter of the neutrophil to a predetermined neutrophil parameter threshold to determine whether an immature granulocyte is present in the neutrophil.
Because the composition structure of the scatter diagram is single and only consists of the distribution arrangement of the pixel points with various colors, the morphological characteristics of the scatter diagram can be extracted on the surface layer based on the relevant means of statistics, such as the number of the types of the pixel points in the scatter diagram, the number of the pixel points with different colors, the average position of the pixel points with different colors, the distance between the pixel points, the distribution dispersion degree and the like.
The morphological parameters are parameters that can reflect morphological characteristics of cells, and can be obtained by calculation based on data in a white blood cell scattergram, for example, the morphological parameters can be mean and variance of the cells at different angles, and can also be the number of corresponding pixels.
Further optionally, the morphological parameters include mean and variance at different angles;
the above step 32 includes:
and if the mean and the variance corresponding to any neutrophil are respectively larger than the mean and the variance in the preset neutrophil parameter threshold, determining the neutrophil as the immature granulocyte.
After the neutrophils have been determined in step 102, the neutrophils may be extracted, morphological parameters of the neutrophils, including mean and variance at different angles, are determined, and then compared to a predetermined neutrophil parameter threshold, all of which are greater than the threshold, to report immature granulocytes. For example, in image processing, neutrophils in a scattergram have three-dimensional data, which includes a plurality of data in one dimension, e.g., 23, 45, 67, from which corresponding means and variances can be calculated.
In an alternative embodiment, the cell types include lymphocytes;
the step 103 includes:
calculating morphological parameters of the lymphocytes;
and comparing the morphological parameters of the lymphocytes with a preset lymphocyte parameter threshold value to determine whether abnormal lymphocytes exist.
Similarly, the lymphocytes are already obtained in the step 102, the lymphocytes may be extracted, morphological parameters of the lymphocytes may also be obtained, and the morphological parameters may also include a mean value and a variance at different angles, and then compared with a preset lymphocyte parameter threshold, and if both of the mean values and the variance are greater than the preset lymphocyte parameter threshold, abnormal lymphocytes are reported, which is not described herein again.
104. And outputting prompt information according to the abnormal cell information.
The prompt information in the embodiment of the application mainly refers to alarm prompt of abnormal cells. Specifically, whether to alarm or not can be determined according to whether the proportion and the number of the abnormal cells reach the preset proportion and the preset number of the abnormal cells or not. For example, if the proportion of one abnormal cell reaches 30% or the number of the abnormal cells reaches 120, the excessive cell quantity can be determined, and corresponding alarm is given; corresponding contents can be prompted by different abnormal conditions, and the embodiment of the application is not limited to the contents.
In an alternative embodiment, the abnormal cell information includes: the proportion and number of first abnormal cells;
the step 104 includes:
if the proportion and the number of the first abnormal cells meet the abnormal prompt condition, outputting first prompt information; or,
and determining whether to output the first prompt message according to the proportion and the number of the first abnormal cells and the proportion and the number of the at least one second abnormal cell.
The first abnormal cell may be any abnormal cell, and may perform corresponding analysis on various abnormal cells, and different abnormal cell types may set different abnormal prompting conditions, such as a corresponding alarm ratio or alarm number, as required, and alarm is performed when a preset value is reached, which is not described herein again.
Optionally, some abnormal cells are connected together on the scatter diagram, and the number and the proportion of second abnormal cells need to be referred to at the same time, where the second abnormal cells may be any other abnormal cells except the first abnormal cells, for example, abnormal lymphocytes refer to the proportion of immature granulocytes, and a corresponding alarm is triggered when the proportion of immature granulocytes exceeds a certain threshold.
In an optional implementation manner, the corresponding relationship between different prompt information and the audit rule may also be set, that is, the audit rule may be set as required, and when an alarm prompt occurs, the corresponding audit rule may be obtained, so that the audit rule may be executed, further checking when the abnormality occurs is realized, and the accuracy of the result is improved. For example, when applied to a sample analyzer, a "Confirmeosinophil" alarm prompt is presented, requiring push microscopy.
In the embodiment of the application, a white blood cell scatter diagram is obtained; processing the white blood cell scatter diagram to obtain a classification result, wherein the classification result is used for representing the cell type in the white blood cell scatter diagram; analyzing based on the cell type to obtain an identification result, wherein the identification result comprises abnormal cell information in the white blood cell scatter diagram; and the prompt information is output according to the abnormal cell information, so that the abnormal sample identification can be realized based on the classification result of the leucocytes, the abnormal sample identification rate is improved, the alarm is effective, the classification result of the leucocytes is not changed by the alarm, the classification is not influenced, and the classification result is not risked.
Referring to fig. 2, fig. 2 is a schematic flow chart of a specific cell analysis method according to an embodiment of the present disclosure. Optionally, the method may be included in the embodiment shown in fig. 1, that is, may be used in step 103 described above. The method comprises the following steps:
201. acquiring the number of overlapped particles of the mononuclear cells and the neutrophils in the white blood cell scatter diagram;
202. the number of overlapping particles is compared to a first number threshold to determine whether immature granulocytes are present or nuclei are left-shifted.
The nucleus left shift mentioned in the examples of the present application is an abnormal neutrophil, which is located between a monocyte and a neutrophil, so that whether the nucleus left shift exists can be judged according to the overlapping of the monocyte and the neutrophil. For example, fig. 3 is a graph of a sample of immature cells and nuclei left shifted according to an embodiment of the present disclosure, wherein the abscissa of the scattergram: the SSC axis, representing the signal intensity of the side scattered light; ordinate: the FSC axis, represents the signal intensity of the forward scattered light.
Specifically, the first number threshold may be set as needed; based on the cell classification of the leukocyte scattergram, the number of overlapping particles in which monocytes and neutrophils are present can be found, and when the number of overlapping particles exceeds a preset first number threshold, it is judged that there is an immature granulocyte or a nucleus is shifted left.
In an optional embodiment, the method further comprises:
obtaining the sum of the ratios of the monocytes and the neutrophils;
the above step 202 includes:
comparing the number of overlapping particles with a second number threshold and the sum of the ratios with a ratio threshold to determine the presence or absence of the immature granulocytes.
Specifically, the number a of overlapping mononuclear cell and neutrophil particles is obtained in step 201, and the sum b of the ratios of mononuclear cells and neutrophil particles may be obtained, and a and b may be compared with preset thresholds (second number threshold and ratio threshold) to determine whether immature granulocytes exist.
Specifically, for example, the second number threshold may be set to 20, and the ratio threshold may be set to 40%, and when a is greater than 20 and b is greater than 40, it is determined that immature granulocytes exist. If a and b do not meet the above conditions in the case where it has been judged that there is an immature granulocyte or a nucleus shift left, it is determined that there is a nucleus shift left.
Through the steps, the existence of immature granulocytes or nucleus left shift can be accurately judged.
Based on the above description of the abnormal sample identification method embodiment, the embodiment of the present application further discloses an abnormal sample identification apparatus, as shown in fig. 4, the abnormal sample identification apparatus 400 includes:
an obtaining module 410, configured to obtain a white blood cell scattergram;
a classification processing module 420 configured to process the white blood cell scattergram to obtain a classification result indicating a cell type in the white blood cell scattergram;
an identification module 430, configured to perform analysis based on the cell types to obtain an identification result, where the identification result includes abnormal cell information in the white blood cell scattergram;
and the prompt module 440 is configured to output prompt information according to the abnormal cell information.
Alternatively, the cell types include neutrophils;
the identification module 430 is specifically configured to:
calculating morphological parameters of the neutrophils;
comparing the morphological parameter of the neutrophil to a predetermined neutrophil parameter threshold to determine whether an immature granulocyte is present in the neutrophil.
Optionally, the morphological parameters include mean and variance at different angles;
the identification module 430 is further specifically configured to:
and if the mean and the variance corresponding to any neutrophil are respectively larger than the mean and the variance in the preset neutrophil parameter threshold, determining the neutrophil as the immature granulocyte.
Alternatively, the cell types include lymphocytes;
the identification module 430 is specifically configured to:
calculating morphological parameters of the lymphocytes;
and comparing the morphological parameters of the lymphocytes with a preset lymphocyte parameter threshold value to determine whether abnormal lymphocytes exist.
Optionally, the identifying module 430 is further specifically configured to:
obtaining the number of overlapping particles of the mononuclear cell and the neutrophil;
the number of overlapping particles is compared to a first number threshold to determine whether immature granulocytes are present or nuclei are left-shifted.
Optionally, the obtaining module 410 is further configured to:
obtaining the sum of the ratios of the monocytes and the neutrophils;
the identifying module 430 is further specifically configured to:
comparing the number of overlapping particles with a second number threshold and the sum of the ratios with a ratio threshold to determine the presence or absence of the immature granulocytes.
Optionally, the abnormal cell information includes: the proportion and number of first abnormal cells;
the prompt module 440 is specifically configured to:
if the proportion and the number of the first abnormal cells meet the abnormal prompt condition, outputting first prompt information; or,
and determining whether to output the first prompt message according to the proportion and the number of the first abnormal cells and the proportion and the number of the at least one second abnormal cell.
According to an embodiment of the present application, each step involved in the methods shown in fig. 1 and fig. 2 may be performed by each module in the abnormal sample identification apparatus 400 shown in fig. 4, and is not described herein again.
The abnormal sample recognition device 400 in the embodiment of the present application may acquire a white blood cell scattergram; processing the white blood cell scatter diagram to obtain a classification result, wherein the classification result is used for representing the cell type in the white blood cell scatter diagram; analyzing based on the cell type to obtain an identification result, wherein the identification result comprises abnormal cell information in the white blood cell scatter diagram; and the prompt information is output according to the abnormal cell information, so that the abnormal sample identification can be realized based on the classification result of the leucocytes, the abnormal sample identification rate is improved, the alarm is effective, the classification result of the leucocytes is not changed by the alarm, the classification is not influenced, and the classification result is not risked.
Based on the description of the method embodiment and the device embodiment, the embodiment of the application also provides a sample analyzer. The sample analyzer may be a blood cell analyzer, and further includes an abnormal sample recognition apparatus 400 as shown in fig. 4.
The above-mentioned abnormal sample recognition apparatus 400 may be a hardware module, or may also be a software system, and may execute any steps in fig. 1 and fig. 2, which is not described herein again. The sample analyzer may further include other components or modules to implement functions such as corresponding sample reaction and sample analysis, and the embodiment of the present application does not limit a specific hardware structure of the sample analyzer.
Optionally, the abnormal sample identification apparatus 400 may also be applied to other electronic devices (terminals) as a software system, and execute the abnormal sample identification method in this embodiment, which is not described herein again.
An embodiment of the present application further provides a computer storage medium (Memory), which is a Memory device in an electronic device and is used to store programs and data. It is understood that the computer storage medium herein may include both a built-in storage medium in the electronic device and, of course, an extended storage medium supported by the electronic device. Computer storage media provide storage space that stores an operating system for an electronic device. Also stored in the memory space are one or more instructions, which may be one or more computer programs (including program code), suitable for loading and execution by the processor. The computer storage medium may be a high-speed RAM memory, or may be a non-volatile memory (non-volatile memory), such as at least one disk memory; and optionally at least one computer storage medium located remotely from the processor.
In one embodiment, one or more instructions stored in a computer storage medium may be loaded and executed by a processor to perform the corresponding steps in the above embodiments; in a specific implementation, one or more instructions in the computer storage medium may be loaded by the processor and perform any step of the method in the embodiments shown in fig. 1 and fig. 2, which is not described herein again.
It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described apparatuses and modules may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. For example, the division of the module is only one logical division, and other divisions may be possible in actual implementation, for example, a plurality of modules or components may be combined or integrated into another system, or some features may be omitted, or not performed. The shown or discussed mutual coupling, direct coupling or communication connection may be an indirect coupling or communication connection of devices or modules through some interfaces, and may be in an electrical, mechanical or other form.
Modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical modules, may be located in one place, or may be distributed on a plurality of network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. The procedures or functions according to the embodiments of the present application are wholly or partially generated when the computer program instructions are loaded and executed on a computer. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored on or transmitted over a computer-readable storage medium. The computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wire (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)), or wirelessly (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device, such as a server, a data center, etc., that includes one or more of the available media. The usable medium may be a read-only memory (ROM), or a Random Access Memory (RAM), or a magnetic medium, such as a floppy disk, a hard disk, a magnetic tape, a magnetic disk, or an optical medium, such as a Digital Versatile Disk (DVD), or a semiconductor medium, such as a Solid State Disk (SSD).
Claims (10)
1. An abnormal sample identification method is characterized by comprising the following steps:
obtaining a white blood cell scatter diagram;
processing the white blood cell scatter diagram to obtain a classification result, wherein the classification result is used for representing the cell type in the white blood cell scatter diagram;
analyzing based on the cell type to obtain an identification result, wherein the identification result comprises abnormal cell information in the white blood cell scatter diagram;
and outputting prompt information according to the abnormal cell information.
2. The abnormal sample identification method according to claim 1, wherein the cell type includes a neutrophil;
said analyzing based on said cell type to obtain an identification result comprising:
calculating morphological parameters of the neutrophils;
comparing the morphological parameter of the neutrophils to a preset neutrophil parameter threshold to determine if immature granulocytes are present in the neutrophils.
3. The abnormal sample identification method according to claim 2, wherein the morphological parameters include mean and variance at different angles;
the comparing the morphological parameter of the neutrophil to a preset neutrophil parameter threshold to determine whether an immature granulocyte is present in the neutrophil, comprising:
and if the mean value and the variance corresponding to any neutrophil are respectively greater than the mean value and the variance in the preset neutrophil parameter threshold, determining the neutrophil as the immature granulocyte.
4. The abnormal sample identification method according to claim 1, wherein the cell types include lymphocytes;
said analyzing based on said cell type to obtain an identification result comprising:
calculating morphological parameters of the lymphocytes;
and comparing the morphological parameters of the lymphocytes with a preset lymphocyte parameter threshold value to determine whether abnormal lymphocytes exist.
5. The abnormal sample identification method according to claim 1, wherein said analyzing based on said cell type to obtain an identification result comprises:
acquiring the number of overlapped particles of the mononuclear cells and the neutrophils;
comparing the number of overlapping particles to a first number threshold to determine whether there is immature granulocytes or nucleus left-shift.
6. The abnormal sample identification method according to claim 5, further comprising:
obtaining the sum of the ratios of the monocytes and the neutrophils;
said comparing said number of overlapping particles to a first number threshold to determine whether there is a premature granulocyte or nucleus left shift, comprising:
comparing the size relationship of the number of overlapping particles to a second number threshold, and the size relationship of the sum of the ratios to a ratio threshold to determine whether the immature granulocytes are present.
7. The abnormal sample identification method according to claim 1, wherein the abnormal cell information includes: the proportion and number of first abnormal cells;
the outputting of the prompt information according to the abnormal cell information includes:
if the proportion and the number of the first abnormal cells meet the abnormal prompt condition, outputting first prompt information; or,
and determining whether to output the first prompt message according to the proportion and the number of the first abnormal cells and the proportion and the number of the at least one second abnormal cell.
8. An abnormal sample recognition apparatus, comprising:
the acquisition module is used for acquiring a white blood cell scatter diagram;
the classification processing module is used for processing the white blood cell scatter diagram to obtain a classification result, and the classification result is used for representing the cell type in the white blood cell scatter diagram;
the identification module is used for analyzing based on the cell type to obtain an identification result, and the identification result comprises abnormal cell information in the white blood cell scatter diagram;
and the prompting module is used for outputting prompting information according to the abnormal cell information.
9. A sample analyzer comprising the abnormal sample recognition apparatus according to claim 8.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, causes the processor to carry out the steps of the method of abnormality sample identification according to any one of claims 1 to 7.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2024102641A1 (en) * | 2022-11-07 | 2024-05-16 | Idexx Laboratories Inc. | Systems and methods for identifying blood conditions via dot plot analysis |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20100104169A1 (en) * | 2008-10-28 | 2010-04-29 | Sysmex Corporation | Specimen processing system and blood cell image classifying apparatus |
| CN103424540A (en) * | 2012-05-18 | 2013-12-04 | 嘉善加斯戴克医疗器械有限公司 | Leukocyte classification kit and classification method thereof |
| CN103492875A (en) * | 2011-04-28 | 2014-01-01 | 希森美康株式会社 | Blood analyzer, blood analysis method, and computer program |
| US20140030729A1 (en) * | 1999-01-25 | 2014-01-30 | Amnis Corporation | Detection of circulating tumor cells using imaging flow cytometry |
| CN106687810A (en) * | 2014-12-31 | 2017-05-17 | 深圳迈瑞生物医疗电子股份有限公司 | Nucleated red blood cell warning method and device, and flow cytometer |
| CN112132831A (en) * | 2020-11-26 | 2020-12-25 | 北京小蝇科技有限责任公司 | White blood cell scatter diagram abnormity joint detection method and system |
| US20210041361A1 (en) * | 2018-04-28 | 2021-02-11 | Shenzhen Mindray Bio-Medical Electronics Co., Ltd. | Alarm method, system and storage medium for abnormalities of sample analyzer |
-
2022
- 2022-03-31 CN CN202210327813.6A patent/CN114419620A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140030729A1 (en) * | 1999-01-25 | 2014-01-30 | Amnis Corporation | Detection of circulating tumor cells using imaging flow cytometry |
| US20100104169A1 (en) * | 2008-10-28 | 2010-04-29 | Sysmex Corporation | Specimen processing system and blood cell image classifying apparatus |
| CN103492875A (en) * | 2011-04-28 | 2014-01-01 | 希森美康株式会社 | Blood analyzer, blood analysis method, and computer program |
| CN103424540A (en) * | 2012-05-18 | 2013-12-04 | 嘉善加斯戴克医疗器械有限公司 | Leukocyte classification kit and classification method thereof |
| CN106687810A (en) * | 2014-12-31 | 2017-05-17 | 深圳迈瑞生物医疗电子股份有限公司 | Nucleated red blood cell warning method and device, and flow cytometer |
| US20210041361A1 (en) * | 2018-04-28 | 2021-02-11 | Shenzhen Mindray Bio-Medical Electronics Co., Ltd. | Alarm method, system and storage medium for abnormalities of sample analyzer |
| CN112132831A (en) * | 2020-11-26 | 2020-12-25 | 北京小蝇科技有限责任公司 | White blood cell scatter diagram abnormity joint detection method and system |
Non-Patent Citations (1)
| Title |
|---|
| 姚东平等: "五分类血液分析仪对诊断白血病的价值", 《检验医学与临床》, no. 05, 14 March 2011 (2011-03-14), pages 578 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2024102641A1 (en) * | 2022-11-07 | 2024-05-16 | Idexx Laboratories Inc. | Systems and methods for identifying blood conditions via dot plot analysis |
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