CN114414442A

CN114414442A - Sample detection method, sample detection device and computer-readable storage medium

Info

Publication number: CN114414442A
Application number: CN202210318329.7A
Authority: CN
Inventors: 石书林; 方建伟; 李国军
Original assignee: Shenzhen Dymind Biotechnology Co Ltd
Current assignee: Shenzhen Dymind Biotechnology Co Ltd
Priority date: 2022-03-29
Filing date: 2022-03-29
Publication date: 2022-04-29

Abstract

The application discloses a sample detection method, a sample detection device and a computer readable storage medium. The method comprises the following steps: determining a set of target particles of a class that is a neutrophil class; forming a particle distribution scatter diagram according to the characteristic signals of the target particle set; projecting scattering points in the particle distribution scattering diagram in a volume dimension to obtain a first curve diagram, wherein the first curve diagram is used for representing the relation between the particle quantity and the particle volume; in response to the first graph having two first peaks, determining particles of the set of target particles having a volume less than a volume corresponding to a first valley between the two first peaks as eosinophils. Through the mode, the classification accuracy of eosinophil granulocytes and neutrophils in the sample to be detected can be improved.

Description

Sample detection method, sample detection device and computer-readable storage medium

Technical Field

The present application relates to the field of sample detection, and more particularly, to a sample detection method, apparatus, and computer-readable storage medium.

Background

The automatic detection of the classification of tiny particles such as cells has great application value in the clinical field and the scientific research field, for example, a blood cell analyzer as a particle analyzer can output information such as the number and volume of red blood cells, white blood cells and platelets in a human blood sample through the recognition of the cells. The particle classification method can be generally classified into an impedance method and a light scattering method according to the principle of counting the detected particles.

The impedance method is based on the coulter principle, and takes a blood cell analyzer as an example, and the instrument analyzes and counts blood cells by measuring impedance changes on electrodes at two ends of a small hole when the blood cells suspended in electrolyte flow through the small hole. The number of pulses between the electrode plates that are energized is detected as the number of cells passing through the aperture, and the intensity of the pulses is proportional to the volume of the cells. Through signal identification and collection, the number and volume of blood cells can be simply analyzed by a specific software system and a classification algorithm. The greatest deficiency of this method is that the blood cells can be grouped only by their size, and leukocytes are classified into five kinds of Lymphocytes (LYM), Monocytes (MONO), Neutrophils (NEU), Eosinophils (EOS), and Basophils (BASO).

The light scattering method can effectively avoid the defects caused by the impedance method, and the general principle is as follows: the blood sample preparation unit conveys a certain amount of diluted sample after reacting with a reagent to a fluid unit, and then the fluid unit conveys the reacted sample to a flow chamber in a detection unit, wherein the flow chamber provides an optical detection area, a blood cell sample flow is wrapped in a sheath flow by using a sheath flow principle in the area, blood cells pass through a detection channel one by one, a light source, usually laser, in the detection unit provides an irradiation light beam to irradiate the detection area of the flow chamber, when the cells flow through the detection area, the irradiation light beam irradiates the cells to generate light scattering and the like, and a two-dimensional scatter diagram is finally formed on a two-dimensional plane according to the size of the pulse by detecting and collecting scattered light in two scattering angle ranges and converting the light signals into electric pulses to output.

Wherein the Low Angle (LAS) scattered light reflects the cell volume size and the Medium Angle (MAS) scattered light reflects the cell internal complexity, and each measured cell is plotted on a two-dimensional scattergram based on two angular direction pulse intensities. Two sheath flows are usually required, the first being a DIFF (Differential leukocyte sorting) channel, which treats the sample with a hemolytic agent to obtain a DIFF scattergram in which lymphocytes, monocytes, neutrophil nuclei and eosinophils can be distinguished. The second time is the BASO (Basophilic granules) channel, in which Basophilic granules were separated from the other four leukocytes in a scatter plot. A blood cell analyzer that uses this detection principle is called a five-class blood cell analyzer.

Disclosure of Invention

The application mainly provides a sample detection method, a sample detection device and a computer readable storage medium, and solves the problem that the classification accuracy of neutrophil granulocytes and eosinophil granulocytes in the particle classification operation in the prior art is low.

In order to solve the above technical problem, a first aspect of the present application provides a sample detection method, including: determining a set of target particles of a class that is a neutrophil class; forming a particle distribution scatter diagram according to the characteristic signals of the target particle set; projecting scattering points in the particle distribution scattering diagram in a volume dimension to obtain a first curve diagram, wherein the first curve diagram is used for representing the relation between the particle quantity and the particle volume; in response to the first graph having two first peaks, determining particles of the set of target particles having a volume less than a volume corresponding to a first valley between the two first peaks as eosinophils.

Optionally, the method further comprises: determining a connecting line of the coordinate point at the upper left corner and the coordinate point at the lower right corner of the particle distribution scatter diagram; projecting the scattered points onto the connecting line to form projection points; determining the distance from the projection point to the coordinate point at the upper left corner; counting the particle number according to the distance to obtain a second curve graph, wherein the second curve graph is used for representing the relation between the distance and the particle number; in response to the second graph having two second peaks, determining particles of the set of target particles having a distance less than a corresponding distance between the two second peaks as eosinophils.

Optionally, the determining a connection line between the coordinate point at the upper left corner and the coordinate point at the lower right corner of the scattergram includes: determining a linear equation of the connecting line as follows: y = -x + b, where b is the maximum value that projects the target set of particles onto the particle distribution scattergram.

Optionally, the determining a distance from the projection point to the upper-left corner coordinate point includes: determining a first distance from a scatter point corresponding to the projection point to the connecting line; determining a second distance from a scatter point corresponding to the projection point to the coordinate point at the upper left corner; and taking the square of the second distance and the square of the variance value of the first distance as the distance from the projection point to the coordinate point at the upper left corner.

Optionally, the first distance is determined using:

and, determining the second distance using:

wherein S1 represents the first distance,

representing the abscissa of the scatter point corresponding to the projection point,

represents the ordinate of the scatter point corresponding to the projected point, and S2 represents the second distance.

Optionally, the method further comprises: determining that the particles in the set of target particles are all neutrophils in response to the second plot not having the second peak or second trough.

Optionally, the method further comprises: carrying out extreme value detection on the first curve graph and determining an extreme value point; determining a peak corresponding to the extreme point in response to the extreme point being the extreme point; and determining the trough corresponding to the extreme point in response to the extreme point being the minimum point.

In order to solve the above technical problem, a second aspect of the present application provides a sample testing device, including: the primary screening module is used for determining a target particle set with the category of the neutrophil; the data processing module is used for forming a particle distribution scatter diagram according to the characteristic signals of the target particle set, and projecting scatter points in the particle distribution scatter diagram in a volume dimension to obtain a first graph, wherein the first graph is used for representing the relation between the particle quantity and the particle volume; and the classification module is used for responding to the existence of two first peaks in the first graph, and determining the particles with the volume smaller than the volume corresponding to the first valley between the two first peaks in the target particle set as the eosinophils.

To solve the above technical problem, a third aspect of the present application provides a sample detection apparatus, including a processor and a memory coupled to each other; the memory has stored therein a computer program for execution by the processor to implement the sample detection method as provided in the first aspect above.

In order to solve the above technical problem, a fourth aspect of the present application provides a computer-readable storage medium, where program data are stored, and when the program data are executed by a processor, the method for detecting a sample provided by the first aspect is implemented.

The beneficial effect of this application is: different from the prior art, the method comprises the steps of determining a target particle set with a neutrophil category, forming a particle distribution scatter diagram according to a characteristic signal of the target particle set, projecting scatter points in the particle distribution scatter diagram in a volume dimension to obtain a first curve diagram, wherein the first curve diagram is used for representing the relationship between the particle number and the particle volume, and finally, responding to the first curve diagram with two first peaks, determining particles with the volume smaller than the volume corresponding to a first valley between the two first peaks in the target particle set as eosinophils. And determining the particles with the volume smaller than the corresponding volume of the wave trough as eosinophils, so that the misclassification of the two particles is reduced, and the particle classification accuracy is improved.

Drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.

FIG. 1 is a schematic block flow diagram of one embodiment of a sample detection method of the present application;

FIG. 2 is a schematic view of an embodiment of a particle distribution scattergram according to the present application;

FIG. 3 is a schematic diagram of an embodiment of a first graph of the present application;

FIG. 4 is a schematic block diagram of a flow chart of another embodiment of a sample detection method of the present application

FIG. 5 is a schematic view of an embodiment of a scattergram according to the present application;

FIG. 6 is a block diagram illustrating the flowchart of an embodiment of step S23;

FIG. 7 is a schematic diagram of an embodiment of a second graph of the present application;

FIG. 8 is a block diagram schematically illustrating the structure of an embodiment of the sample detection apparatus of the present application;

FIG. 9 is a block diagram schematically illustrating the structure of another embodiment of the sample detection device of the present application

FIG. 10 is a block diagram illustrating the structure of one embodiment of the computer-readable storage medium of the present application.

Detailed Description

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" and "second" in this application are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present application, "plurality" means at least two, e.g., two, three, etc., unless explicitly specifically limited otherwise. 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. Those skilled in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.

Although five kinds of cells, i.e., Lymphocytes (LYM), Monocytes (MONO), Neutrophils (NEU), Eosinophils (EOS), and Basophils (BASO), can be distinguished by the light scattering method, when eosinophils are increased in a sample, or the morphology of cells in the sample is changed due to a disease or the like, the cells become larger as a whole, the formed scatter diagram is shifted as a whole, the boundary between eosinophils and neutrophils is blurred, classification recognition is affected, and classification and counting of eosinophils and neutrophils are not easy.

Referring to fig. 1, fig. 1 is a schematic flow chart of an embodiment of a sample detection method according to the present application. It should be noted that, if the result is substantially the same, the flow sequence shown in fig. 1 is not limited in this embodiment. The embodiment comprises the following steps:

step S11: and determining the target particle set with the category of the neutrophil in the sample to be tested.

This step preliminarily determines particles belonging to the neutrophil class among a large number of particles in the sample to be tested, and uses such particles as the operation target of the present embodiment, i.e., the target particle set as described above. The test sample includes at least neutrophils and eosinophils, such as a blood sample.

In a sample testing scenario, for example, the present invention is applied to a blood cell analyzer, which can classify and count white blood cells, red blood cells, and hemoglobin in a blood sample. In this embodiment, for example, a test sample including at least neutrophils and eosinophils is subjected to particle classification, and specifically, the cells in the test sample are prepared into a suspension of single cells by using a flow cytometry, and the suspension is placed in a sample tube after being stained by a specific dye and enters a flow chamber through a nozzle. The flowing chamber is filled with the sheath liquid under certain pressure, the cells are arranged in a single row to pass through the light transmission detection part of the flowing chamber under the constraint of the sheath liquid, the laser irradiates on the single cell to trigger the change of a light scattering field, and the information of the cell volume, the complexity and the like can be provided. And then, a group of two-dimensional data of corresponding cells is obtained through photoelectric conversion and AD conversion, and the two-dimensional data respectively represent volume information and internal complexity information of the particles. And mapping the group of two-dimensional data to a two-dimensional coordinate system to obtain the corresponding position of the leucocyte in the two-dimensional coordinate system. By analogy, a two-dimensional leukocyte scatter diagram can be obtained, in the two-dimensional leukocyte scatter diagram, particles of the same type are gathered together, particles of different types are separated from each other, and the particles belonging to the neutrophil category can be determined by dividing the areas where the various types of cell particles are located. Thereby determining a set of target particles.

Step S12: and forming a particle distribution scatter diagram according to the characteristic signals of the target particle set.

The characteristic signal is, for example, a scattered light signal emitted by the particle under laser irradiation, and may include volume information, internal complexity information, and the like of the particle, the volume information and the internal complexity information of the particle are extracted, the two-dimensional information is mapped onto a two-dimensional coordinate system, the position of the particle under the two-dimensional coordinate system is obtained, and a particle distribution scattergram is formed by collecting all particles of the target particle set under the two-dimensional coordinate system. Each scatter point in the particle distribution scatter plot may be represented by coordinates (X, Y), where X represents the coordinates of the scatter point in the X-axis direction and Y represents the coordinates of the scatter point in the Y-axis direction.

For example, referring to fig. 2, fig. 2 is a schematic view of an embodiment of a particle distribution scattergram according to the present application. The scatter in the graph indicates the scatter corresponding to each particle in the set of target particles, and EOS indicated by an arrow is approximately an eosinophil distribution region, and is close to a neutrophil distribution region, and the distribution boundaries of the two types of particles are unclear.

Step S13: and projecting the scatter points in the particle distribution scatter diagram in the volume dimension to obtain a first curve diagram.

The first graph is used to represent the relationship between the number of particles and the volume of particles. Specifically, the number of particles in each volume size can be obtained by projecting the scatter point in the volume dimension, the abscissa of the first graph can represent the volume, and the ordinate represents the number of particles. For example, please refer to fig. 3, fig. 3 is a schematic diagram of an embodiment of a first graph of the present application.

Step S14: in response to the first graph having two first peaks, determining particles of the target set of particles having a volume less than a volume corresponding to a first valley between the two first peaks as eosinophils.

Referring to fig. 3, peak and valley detection is performed on the first graph, and if two first peaks, P1 and P2, exist in the first graph, and a first valley exists between the two first peaks P1 and P2, and a corresponding volume of the valley is, for example, a, it is determined that the volume of the target particle set is less than a, which is a eosinophil.

It is understood that after this step, the particles with the volume larger than a in the target particle set can be finally determined as neutrophils, and such particles can be further classified, so as to further improve the accuracy of particle classification.

In one embodiment, the detection of the peaks and valleys is performed on the first graph by: carrying out extreme value detection on the first curve graph and determining an extreme value point; judging the type of the extreme point of each extreme point, and determining the peak corresponding to the extreme point if the extreme point is the extreme point; and when the extreme point is the minimum point, determining the trough corresponding to the extreme point.

In further embodiments, the detection of peaks and valleys may also be performed on the first graph by: carrying out forward difference or backward difference on the first curve graph, wherein if adjacent data in the difference result is a positive place and a negative place, the adjacent data is an extreme point; and determining the magnitude relation between the data and two adjacent data at the extreme value point, wherein a data point larger than the two adjacent data is determined as a peak, and a data point smaller than the two adjacent data is determined as a trough.

Before step S14, the first graph is subjected to a filtering process, and the filtering process can remove noise in the first graph, so that the result of the first graph for particle counting is more accurate. The filtering processing method may be one of mean filtering, median filtering, and gaussian filtering.

Different from the prior art, in the embodiment, after the target particle set of which the type is the neutrophil type is preliminarily determined, the target particle set is processed again, two-dimensional data of each particle is extracted, a particle distribution scatter diagram is prepared, the particles are projected on a volume dimension, a curve diagram representing the relationship between the number of the particles and the volume is obtained, eosinophils in the target particle set are determined according to identification of peaks and troughs of the curve diagram, the accuracy of particle classification is improved, and classification deviation caused by unclear boundaries of the eosinophils and the neutrophils in the scatter diagram is reduced.

Referring to fig. 4, fig. 4 is a schematic flow chart diagram of another embodiment of the sample detection method of the present application. It should be noted that, if the result is substantially the same, the flow sequence shown in fig. 4 is not limited in this embodiment. The embodiment comprises the following steps:

step S21: and determining a connecting line of the coordinate point at the upper left corner and the coordinate point at the lower right corner of the particle distribution scatter diagram.

The upper left coordinate point and the lower right coordinate point of the particle distribution scatter diagram are respectively a coordinate point represented by the maximum value of the particle distribution scatter diagram on the vertical axis and a coordinate point represented by the maximum value on the horizontal axis. Referring to fig. 2, the coordinate point at the top left corner is point a, and the coordinate point at the bottom right corner is point C.

This maximum is the maximum value at which the particle is projected onto the particle distribution scattergram, e.g., if the electrical signal values of the particles in the target particle set range from 0 to 4094, then the projection is the maximum value of about 128 as the electrical signal values of the particles are divided by 32.

In some embodiments, the equation of the straight line where the connecting line is located can be directly determined as:

y=-x+b

wherein b is the maximum value projected from the target particle set to the particle distribution scatter diagram.

Step S22: and projecting the scattered points onto the connecting line to form projection points.

Step S23: and determining the distance from the projection point to the coordinate point at the upper left corner.

Referring to fig. 5, fig. 5 is a schematic diagram of an embodiment of a scatter point projection according to the present application. Wherein, B represents an origin coordinate point of the particle distribution scatter diagram, AC represents a connecting line of a coordinate point at the upper left corner and a coordinate point at the lower right corner of the particle distribution scatter diagram, D represents a scatter point, F represents a projection point for projecting the scatter point D onto the connecting line AC, and E represents a projection point for projecting the scatter point onto a vertical coordinate.

To determine the distance from the projection point F to the coordinate point A at the upper left corner of the particle distribution scatter diagram, the two-dimensional coordinates of the scatter point D can be determined

And the equation of the line on which AC lies (y = -x + b).

Referring to fig. 6, fig. 6 is a schematic block diagram illustrating a flow of step S23 according to an embodiment of the present application.

Step S231: and determining a first distance from the scattered point corresponding to the projection point to the connecting line.

This step, i.e. determining the distance between point D and point F, may determine the first distance using the following equation:

step S232: and determining a second distance from the scatter point corresponding to the projection point to the coordinate point at the upper left corner.

This step, i.e. determining the distance between point D and point a, may determine the second distance using the following equation:

in the above two equations, S1 represents the first distance,

the abscissa of the scatter point corresponding to the projection point is represented,

indicating the ordinate of the scatter point corresponding to the projected point, and S2 indicating the second distance.

Step S233: and taking the square of the second distance and the square of the variance value of the first distance as the distance from the projection point to the coordinate point at the upper left corner.

Namely, the distance from the projection point F to the coordinate point A at the upper left corner is determined

。

Step S24: and counting the number of the particles according to the distance to obtain a second curve graph.

The second graph is used to show the relationship between distance and particle number. Specifically, the distance between the projection point of each scatter point and the coordinate point at the upper left corner of the scatter diagram is determined according to the steps, the number of particles at each distance is counted, and a second graph is formed, wherein the horizontal axis of the second graph represents the distance, and the vertical axis represents the number of particles.

Step S25: in response to the second graph having two second peaks, determining particles of the set of target particles having a corresponding distance less than a corresponding distance of a second valley between the two second peaks as eosinophils.

Referring to fig. 7, fig. 7 is a schematic diagram of an embodiment of a second graph of the present application. Similarly, the peak and valley detection is performed on the second graph, and if two second peaks exist in the second graph, which are P3 and P4, respectively, and a second valley exists between the two second peaks P3 and P4, and the distance corresponding to the second valley is S, for example, the particle with the corresponding projection point distance smaller than S in the target particle set is determined as the eosinophil.

After this step, the particles whose corresponding projected point distance is greater than S in the target particle set can be finally determined as neutrophils, or the particles can be determined as a new target particle set, and the neutrophils and eosinophils therein are further distinguished by using the above-mentioned methods of the embodiments, so as to improve the particle classification accuracy.

It is to be understood that, in the absence of the second peak or the second valley in the second graph, the particles in the target set of particles are all determined to be neutrophils.

The way of detecting the peak and the trough of the second graph is, for example, an extreme method, a difference method, etc., and reference may be made to the detailed description of step S14 in the previous embodiment, which is not repeated herein.

Different from the prior art, in the embodiment, by establishing a connection line between an upper left-corner coordinate point and a lower right-corner coordinate point of a particle distribution scatter diagram, projecting each scatter point onto the connection line, and counting a second curve graph related to the number of particles according to a distance between a projection point and the upper left-corner coordinate point, considering a distance difference between a projection point formed after scatter points corresponding to eosinophilic granulocytes and neutrophils are projected onto the connection line and the upper left-corner coordinate point, eosinophilic granulocytes in a target particle set are distinguished, and the particle classification accuracy is high.

It is to be understood that the above embodiments may be implemented individually or in combination. Specifically, the peak and valley detection is performed on the first graph before step S14, and if there is no peak or valley, steps S21 to S25 are performed to continue the particle classification in steps S21 to S25.

Referring to fig. 8, fig. 8 is a schematic block diagram of a sample detection device according to an embodiment of the present disclosure. The sample detection apparatus 100 includes: the system comprises a primary screening module 101, a data processing module 102 and a classification module 103, wherein the primary screening module 101 is used for determining a target particle set with a category of neutrophil cells; the data processing module 102 is configured to form a particle distribution scattergram according to the characteristic signal of the target particle set, and project scatters in the particle distribution scattergram in a volume dimension to obtain a first graph, where the first graph is used to represent a relationship between a particle number and a particle volume; the classification module 103 is configured to determine, as eosinophils, particles in the target particle set whose volume is smaller than a volume corresponding to a first valley between two first peaks in response to the presence of the two first peaks in the first graph.

The data processing module 102 may further be configured to determine a connection line between the upper-left coordinate point and the lower-right coordinate point of the particle distribution scattergram; projecting the scattered points onto the connecting line to form projection points; determining the distance from the projection point to the coordinate point at the upper left corner; and counting the particle number according to the distance to obtain a second curve graph, wherein the second curve graph is used for representing the relation between the distance and the particle number. The classification module 103 may be further configured to determine, as eosinophils, particles in the target particle set that are less than a distance corresponding to a second valley between two second peaks in response to the presence of the two second peaks in the second graph.

The data processing module 102 may be further configured to determine a first distance from a scatter point corresponding to the projection point to the connection line; determining a second distance from a scatter point corresponding to the projection point to the coordinate point at the upper left corner; and taking the square of the second distance and the square of the variance value of the first distance as the distance from the projection point to the coordinate point at the upper left corner.

Classification module 103 may be further to determine that the set of target particles are all neutrophils in response to the second plot not having the peak or the second trough.

The data processing module 102 may be further configured to perform extremum detection on the first graph, determine an extremum point, determine a peak corresponding to the extremum point in response to the extremum point being the extremum point, and determine a trough corresponding to the extremum point in response to the extremum point being the minima point.

For the detailed manner of each step executed by each process, please refer to the description of each step in the embodiment of the sample detection method of the present application, which is not repeated herein.

The sample detection apparatus 100 is, for example, a blood cell analyzer, and is used for classifying and counting particles in a blood sample.

Referring to fig. 9, fig. 9 is a schematic block diagram of another embodiment of a sample detection device according to the present application. The sample detection device 200 comprises a processor 210 and a memory 220 coupled to each other, wherein the memory 220 stores a computer program, and the processor 210 is configured to execute the computer program to implement the sample detection method according to the above embodiments.

For the description of the steps executed in the processing, refer to the description of the steps in the embodiment of the sample detection method of the present application, which is not repeated herein.

The memory 220 may be used to store program data and modules, and the processor 210 executes various functional applications and data processing by operating the program data and modules stored in the memory 220. The memory 220 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application program required by at least one function (such as a signal processing function, a scatter projection function, a graph statistics function, etc.), and the like; the storage data area may store data (such as particle characteristic signals, scatter coordinates, particle distribution scattergrams, graphs, etc.) created according to the use of the sample detection device 200, and the like. Further, the memory 220 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid state storage device. Accordingly, the memory 220 may also include a memory controller to provide the processor 210 with access to the memory 220.

In the embodiments of the present application, the disclosed method and apparatus may be implemented in other ways. For example, the various embodiments of the sample testing device 200 described above are merely illustrative, and for example, the division of the modules or units is merely a logical division, and other divisions may be realized, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.

The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the embodiment.

In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.

The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application may be embodied in the form of a software product, which is stored in a storage medium, or in a part of or all of the technical solutions that contribute to the prior art.

Referring to fig. 10, fig. 10 is a schematic block diagram illustrating a structure of an embodiment of a computer-readable storage medium 300 of the present application, where the computer-readable storage medium 300 stores program data 310, and the program data 310 implements the steps of the embodiments of the sample detection method described above when executed.

The computer-readable storage medium 300 may be a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and various media capable of storing program codes.

The above description is only an example of the present application and is not intended to limit the scope of the present application, and all modifications of equivalent structures and equivalent processes, which are made by the contents of the specification and the drawings, or which are directly or indirectly applied to other related technical fields, are intended to be included within the scope of the present application.

Claims

1. A method for detecting a sample, the method comprising:

determining a target particle set with the category of neutrophil in a sample to be detected;

forming a particle distribution scatter diagram according to the characteristic signals of the target particle set;

projecting scattering points in the particle distribution scattering diagram in a volume dimension to obtain a first curve diagram, wherein the first curve diagram is used for representing the relation between the particle quantity and the particle volume;

in response to the first graph having two first peaks, determining particles of the set of target particles having a volume less than a volume corresponding to a first valley between the two first peaks as eosinophils.

2. The method of claim 1, further comprising:

determining a connecting line of the coordinate point at the upper left corner and the coordinate point at the lower right corner of the particle distribution scatter diagram;

projecting the scattered points onto the connecting line to form projection points;

determining the distance from the projection point to the coordinate point at the upper left corner;

counting the particle number according to the distance to obtain a second curve graph, wherein the second curve graph is used for representing the relation between the distance and the particle number;

in response to the second graph having two second peaks, determining particles of the set of target particles having a distance less than a corresponding distance between the two second peaks as eosinophils.

3. The method of claim 2, wherein determining a line connecting the upper-left coordinate point and the lower-right coordinate point of the particle distribution scatter diagram comprises:

determining a linear equation of the connecting line as follows:

y=-x+b

wherein b is the maximum value of the projection of the target particle set onto the particle distribution scattergram.

4. The method of claim 3, wherein determining the distance from the proxel to the upper-left corner coordinate point comprises:

determining a first distance from a scatter point corresponding to the projection point to the connecting line;

determining a second distance from a scatter point corresponding to the projection point to the coordinate point at the upper left corner;

and taking the square of the difference value of the second distance square and the first distance square as the distance from the projection point to the coordinate point at the upper left corner.

5. The method of claim 4, wherein the first distance is determined using the equation:

and determining the second distance using:

wherein S1 represents the first distance,

6. The method of claim 2, further comprising:

determining that the particles in the set of target particles are all neutrophils in response to the second plot not having the second peak or second trough.

7. The method of claim 1, further comprising:

carrying out extreme value detection on the first curve graph and determining an extreme value point;

determining a peak corresponding to the extreme point in response to the extreme point being the extreme point;

and determining the trough corresponding to the extreme point in response to the extreme point being the minimum point.

8. A sample testing device, comprising:

the primary screening module is used for determining a target particle set with the category of the neutrophil;

the data processing module is used for forming a particle distribution scatter diagram according to the characteristic signals of the target particle set, and projecting scatter points in the particle distribution scatter diagram in a volume dimension to obtain a first graph, wherein the first graph is used for representing the relation between the particle quantity and the particle volume;

and the classification module is used for responding to the existence of two first peaks in the first graph, and determining the particles with the volume smaller than the volume corresponding to the first valley between the two first peaks in the target particle set as the eosinophils.

9. A sample detection device, comprising a processor and a memory coupled to each other; the memory has stored therein a computer program for execution by the processor to implement the steps of the method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores program data which, when executed by a processor, implements the steps of the method according to any one of claims 1-7.