A method, device, equipment and storage medium for dividing lipoprotein subtype components

Abstract

The present invention discloses a method, apparatus, device, and storage medium for lipoprotein subtype component segmentation. The method comprises: obtaining a reagent scan image of a lipoprotein reagent to be segmented; and segmenting the reagent scan image into low-density lipoprotein subtype components based on predetermined target segmentation points, wherein the target segmentation points are obtained by processing sample scan images of multiple sample lipoprotein reagents. By using a portion of lipoprotein reagents with more distinct features as sample lipoprotein reagents, statistical analysis is performed on the reagent image to ultimately determine the segmentation points. The determined segmentation points are then applied to the reagent scan images of the lipoprotein reagents to be segmented, enabling accurate and automated batch segmentation of lipoprotein subtype components.

Description

Technical Field

[0001] The present invention relates to the technical field of lipoprotein subtype component classification, and in particular to a lipoprotein subtype component classification method, device, equipment and storage medium. Background Art

[0002] Low-density lipoprotein (LDL) is the primary carrier of cholesterol in the body and can be divided into seven types. However, excessive LDL levels can trigger an inflammatory response. Oxidized LDL, after being engulfed by macrophages, can form fatty plaques in blood vessels, leading to dyslipidemia or arteriosclerosis. Smaller LDLs are more susceptible to oxidation, forming plaques on blood vessel walls. LDL types 1 and 2 (large LDL) are considered "normal LDL" and are responsible for normal cholesterol transport. LDL types 3 to 7 (small LDL) are considered "bad or abnormal LDL" and are easily oxidized, contributing to cardiovascular disease. The relationship between "small" LDL levels and coronary artery disease has been established. Measuring total LDL alone does not indicate cardiovascular disease risk because it cannot distinguish between "large" and "small" LDL. Even if total LDL levels are normal, an elevated level of small, dense (3-7) LDL can more than triple the risk of cardiovascular disease. Discovering the risk of disease at an early stage can significantly prevent and treat the occurrence of cardiovascular disease.

[0003] There is no complete standard for the classification of LDL subgroups worldwide, which leads to inaccurate classification of LDL subgroups. Summary of the Invention

[0004] The present invention provides a method, device, equipment and storage medium for classifying lipoprotein subtype components to solve the technical problem of inaccurate classification of LDL subtypes.

[0005] According to one aspect of the present invention, a method for classifying lipoprotein subtypes is provided, comprising:

[0006] acquiring a reagent scanning image of the reagent to be divided into lipoproteins;

[0007] The reagent scanning image is divided into low-density lipoprotein subtype components based on predetermined target segmentation points, and the target segmentation points are obtained based on sample scanning images of multiple sample lipoprotein reagents.

[0008] Optionally, based on the above solution, it also includes:

[0009] Acquiring sample scan images of multiple sample lipoprotein reagents, and processing the sample scan images to obtain a single sample image;

[0010] The target segmentation point corresponding to the single sample image is determined according to the gray value waveform image of the single sample image.

[0011] Optionally, based on the above solution, determining the target segmentation point corresponding to the single sample image according to the gray value waveform image of the single sample image includes:

[0012] Determine a single demarcation point corresponding to a single sample image according to a gray value waveform image of the single sample image;

[0013] Based on each individual dividing point, the target segmentation point is determined.

[0014] Optionally, based on the above solution, determining a single demarcation point corresponding to a single sample image according to a gray value waveform image of the single sample image includes:

[0015] The grayscale waveform image is divided into waveforms, and the connection points of adjacent waveforms are used as single dividing points.

[0016] Optionally, based on the above solution, the target segmentation points are determined based on each individual dividing point, including:

[0017] Conduct statistical verification on each individual cut-off point and determine the confidence interval of each individual cut-off point;

[0018] Determine the component splitting ratio of each individual dividing point based on the confidence interval of each individual dividing point;

[0019] Determine the target segmentation point corresponding to a single dividing point according to the component segmentation ratio.

[0020] Optionally, based on the above scheme, the component splitting ratio of each single demarcation point is determined based on the confidence interval of each single demarcation point, including:

[0021] The middle value of the confidence interval is used as the component split ratio of a single dividing point.

[0022] Optionally, based on the above solution, the target segmentation point corresponding to a single demarcation point is determined according to the component segmentation ratio, including:

[0023] Determine the total length of the distance from the peak of very low-density lipoprotein as the starting point to the peak of high-density lipoprotein as the end point in a single sample image;

[0024] The target segmentation point is obtained based on the component segmentation ratio and the total distance.

[0025] According to another aspect of the present invention, there is provided a device for classifying lipoprotein subtypes, comprising:

[0026] A reagent image acquisition module, used for acquiring a reagent scanning image of the reagent to be divided into lipoproteins;

[0027] The reagent image segmentation module is used to segment the reagent scan image into low-density lipoprotein subtype components based on predetermined target segmentation points, and the target segmentation points are obtained based on sample scan images of multiple sample lipoprotein reagents.

[0028] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0029] at least one processor; and

[0030] a memory communicatively connected to at least one processor; wherein,

[0031] The memory stores a computer program that can be executed by at least one processor. The computer program is executed by at least one processor so that the at least one processor can execute the lipoprotein subtype component classification method according to any embodiment of the present invention.

[0032] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions for enabling a processor to implement the method for classifying lipoprotein subtype components according to any embodiment of the present invention when the computer instructions are executed.

[0033] The technical solution of the embodiment of the present invention obtains a reagent scan image of the lipoprotein reagent to be segmented; then segments the reagent scan image into low-density lipoprotein subtype components based on predetermined target segmentation points, which are obtained by processing sample scan images of multiple sample lipoprotein reagents. By using a portion of lipoprotein reagents with more distinct features as sample lipoprotein reagents, statistical analysis is performed on the reagent image to ultimately determine the segmentation points. The determined segmentation points are then applied to the reagent scan images of the lipoprotein reagents to be segmented, enabling accurate and automated batch segmentation of lipoprotein subtype components.

[0034] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present invention, nor is it intended to limit the scope of the present invention. Other features of the present invention will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0036] Figure 1This is a flow chart of a method for classifying lipoprotein subtypes provided in Example 1 of the present invention;

[0037] Figure 2a This is a flow chart of a method for classifying lipoprotein subtypes provided in Example 2 of the present invention;

[0038] Figure 2b is a schematic diagram of a sample scan image and a single sample image provided by the second embodiment of the present invention;

[0039] Figure 2c This is a schematic diagram of segmenting a grayscale waveform image provided by the second embodiment of the present invention;

[0040] Figure 2d Schematic diagram of a confidence interval of a single demarcation point provided in the second embodiment of the present invention;

[0041] Figure 2e Schematic diagram of a confidence interval of a single demarcation point provided in the second embodiment of the present invention;

[0042] Figure 3 This is a schematic structural diagram of a device for classifying lipoprotein subtypes provided in Example 3 of the present invention;

[0043] Figure 4 This is a structural diagram of an electronic device provided in Example 4 of the present invention. DETAILED DESCRIPTION

[0044] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0045] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0046] Example 1

[0047] Figure 1 This is a flow chart of a lipoprotein subtype component classification method provided by the first embodiment of the present invention. This embodiment is applicable to the classification of low-density lipoprotein subtype components. The method can be executed by a lipoprotein subtype component classification device. The lipoprotein subtype component classification device can be implemented in the form of hardware and / or software. The lipoprotein subtype component classification device can be configured in an electronic device. Figure 1 As shown, the method includes:

[0048] S110 , obtaining a reagent scanning image of the reagent to be used for lipoprotein separation.

[0049] S120 , dividing the reagent scan image into low-density lipoprotein subtype components based on predetermined target segmentation points, wherein the target segmentation points are obtained by processing sample scan images of multiple sample lipoprotein reagents.

[0050] In the embodiments of the present invention, image processing is performed in advance based on a lipoprotein reagent having distinct characteristics to determine target segmentation points, i.e., division points, for LDL subclassification. When performing lipoprotein subclassification, the predetermined target segmentation points are ultimately applied to the reagent scan image to achieve automated segmentation. The division of the LDL subclasses in the reagent scan image based on the target segmentation points can refer to prior art methods for lipoprotein subclassification, and is not limited herein.

[0051] Optionally, a reagent scanning image of the lipoprotein reagent to be divided after electrophoresis can be obtained, grayscale values in the reagent scanning image can be counted to obtain a reagent grayscale value statistical graph of the reagent scanning image, and the reagent grayscale value statistical graph can be divided based on the target segmentation points.

[0052] The technical solution of the embodiment of the present invention obtains a reagent scan image of the lipoprotein reagent to be segmented; then segments the reagent scan image into low-density lipoprotein subtype components based on predetermined target segmentation points, which are obtained by processing sample scan images of multiple sample lipoprotein reagents. By using a portion of lipoprotein reagents with more distinct features as sample lipoprotein reagents, statistical analysis is performed on the reagent image to ultimately determine the segmentation points. The determined segmentation points are then applied to the reagent scan images of the lipoprotein reagents to be segmented, enabling accurate and automated batch segmentation of lipoprotein subtype components.

[0053] Example 2

[0054] Figure 2 is a flow chart of a method for dividing lipoprotein subtype components provided by Example 2 of the present invention. Based on the above embodiment, this embodiment optimizes the determination of the target segmentation point, such as Figure 2a As shown, the method includes:

[0055] S210 , obtaining sample scan images of a plurality of sample lipoprotein reagents, and processing the sample scan images to obtain a single sample image.

[0056] In this embodiment, a small batch of lipoprotein reagents with obvious characteristics are used as sample lipoprotein reagents, and reagent scanning images of the sample lipoprotein reagents after electrophoresis are obtained as sample scanning images.

[0057] In one implementation, processing the original scanned image to obtain a single sample image includes: segmenting the original scanned image by extracting a valid region to obtain a plurality of the single sample images. Optionally, separating the reagents in the sample scanned image and extracting the valid regions thereof, and then obtaining an image of the single reagent image as the single sample image.

[0058] Figure 2b This is a schematic diagram of a sample scan image and a single sample image provided in the second embodiment of the present invention. Figure 2b The left part of the middle arrow is a schematic diagram of the sample scan image. Figure 2b The part to the right of the middle arrow is a schematic diagram of a single scan image.

[0059] S220 : Determine a target segmentation point corresponding to the single sample image according to the grayscale value waveform image of the single sample image.

[0060] In this embodiment, by performing gray value analysis on a captured single sample image, a gray value waveform image can be drawn, and target segmentation points can be determined from the gray value waveform image according to certain mathematical rules.

[0061] In general, mathematical rules are used to find the segmentation points of each component of low-density lipoprotein (LDL) on the grayscale waveform image, and the peaks of very low-density lipoprotein (VLDL) and high-density lipoprotein (HDL) are used as the starting and ending points to calculate the percentage. Then, the data of each segmentation point of the batch are statistically analyzed, and its confidence interval is calculated. The target segmentation point is determined based on the confidence interval.

[0062] In one embodiment of the present invention, determining the target segmentation point corresponding to the single sample image based on the grayscale value waveform image of the single sample image includes: determining a single demarcation point corresponding to the single sample image based on the grayscale value waveform image of the single sample image; and determining the target segmentation point based on each of the single demarcation points. After generating the grayscale value waveform image, finding a single image segmentation line according to a certain mathematical rule, determining a single demarcation point based on the single image segmentation line, and then determining the target segmentation point based on the single demarcation point.

[0063] Figure 2c This is a schematic diagram of segmenting a grayscale waveform image provided by the second embodiment of the present invention. Figure 2c As shown in the figure, the vertical axis is the statistical gray value, and the horizontal axis represents the position, corresponding to a single sample image. Figure 2c The two highest peaks are the peak of high-density lipoprotein HDL and the peak of very low-density lipoprotein VLDL, respectively. The remaining dotted dividing lines are the dividing lines found by mathematical rules. The dividing points corresponding to the remaining dotted dividing lines are the single dividing points corresponding to a single sample image.

[0064] In one implementation, determining the single demarcation point corresponding to the single sample image based on the grayscale value waveform image of the single sample image includes: performing waveform segmentation on the grayscale value waveform image and using the connection point of adjacent waveforms as the single demarcation point. Multiple waveforms contained in the grayscale value waveform image can be identified, and the connection point of adjacent waveforms can be used as the single demarcation point.

[0065] In one embodiment of the present invention, the target segmentation point is determined based on each of the single demarcation points, including: statistical verification of each of the single demarcation points to determine the confidence interval of each of the single demarcation points; determining the component segmentation ratio of the single demarcation point based on the confidence interval of each of the single demarcation points; and determining the target segmentation point corresponding to the single demarcation point according to the component segmentation ratio. The coordinates of multiple single demarcation points of a single sample image can be searched in batches, and the percentage of the total length of each image in the batch is obtained based on the coordinates of each single demarcation point, where the total length refers to the distance between the two highest peaks in the grayscale value waveform image. The statistical percentage points are then statistically verified, and all points conform to the normal distribution after boxcox transformation, and the confidence interval of each single demarcation point is calculated on this basis. The component segmentation ratio corresponding to the single demarcation point is then determined based on the confidence interval, and finally the target segmentation point corresponding to the single demarcation point is determined based on the component segmentation ratio. The target segmentation point corresponding to each single demarcation point is determined in sequence by the above method until the target segmentation points corresponding to all single demarcation points in all single sample images are obtained.

[0066] Determining the component split ratio corresponding to a single demarcation point based on a confidence interval may include determining an eigenvalue of the confidence interval based on the upper and lower limits of the confidence interval, and using the eigenvalue of the confidence interval as the component split ratio corresponding to the single demarcation point. The eigenvalue of the confidence interval may be a weighted sum of the upper and lower limits.

[0067] Figure 2dThis is a schematic diagram of a confidence interval of a single dividing point provided in Example 2 of the present invention. Figure 2e This is a schematic diagram of a confidence interval of a single dividing point provided in Example 2 of the present invention. Figure 2d and Figure 2e The confidence intervals of two individual cut-off points are schematically shown in FIG.

[0068] In one implementation, the component splitting ratio of each single demarcation point is determined based on the confidence interval of each single demarcation point, including: taking the middle value of the confidence interval as the component splitting ratio of the single demarcation point. Optionally, the middle value of the confidence interval, that is, the average value of the upper limit and the lower limit of the confidence interval, is taken as the component splitting ratio of the single demarcation point. Figure 2d As shown, 36.5% can be taken as Figure 2d The component division ratio corresponding to a single dividing point in Figure 2e As shown, 47.3% can be taken as Figure 2e The component division ratio corresponding to a single dividing point in .

[0069] In one embodiment of the present invention, the target segmentation point corresponding to the single demarcation point is determined according to the component segmentation ratio, including: determining the total length of the distance from the peak of very low density lipoprotein as the starting point to the peak of high density lipoprotein as the end point in the single sample image; obtaining the target segmentation point based on the component segmentation ratio and the total length of the distance. Optionally, the peak of VLDL in each waveform is used as the starting point, the peak of HDL is used as the end point, and the distance between the two points is set as the total length. The distance of the corresponding segmentation point relative to the remaining starting point can be obtained by multiplying the two percentage points in the above example by the total length, and the coordinates of the starting point are added to obtain the coordinates of the segmentation point, that is, the coordinates of the target segmentation point. The target segmentation points corresponding to the remaining single demarcation points can be calculated using the same method as above.

[0070] S230: Obtain a reagent scanning image of the reagent to be used for lipoprotein separation.

[0071] S240 , dividing the reagent scan image into low-density lipoprotein subtype components based on predetermined target segmentation points.

[0072] The technical solution of this embodiment is to obtain sample scan images of multiple sample lipoprotein reagents, process the sample scan images to obtain a single sample image; determine the target segmentation points corresponding to the single sample image based on the grayscale value waveform image of the single sample image; and use a portion of lipoprotein reagents with more obvious features as sample lipoprotein reagents to perform statistical analysis on the reagent images to ultimately determine the target segmentation points, so that accurate and automated batch segmentation of lipoprotein subtype components can be achieved based on the determined target segmentation points applied to the reagent scan images of the lipoprotein reagents to be divided.

[0073] Example 3

[0074] Figure 3 This is a schematic diagram of the structure of a lipoprotein subtype component classification device provided by Example 3 of the present invention. Figure 3 As shown, the device includes:

[0075] The reagent image acquisition module 310 is used to acquire a reagent scanning image of the reagent to be separated into lipoproteins;

[0076] The reagent image segmentation module 320 is used to segment the reagent scan image into low-density lipoprotein subtype components based on predetermined target segmentation points, where the target segmentation points are obtained based on sample scan images of multiple sample lipoprotein reagents.

[0077] The technical solution of this embodiment obtains a reagent scan image of the lipoprotein reagent to be segmented; then segments the reagent scan image into low-density lipoprotein subtypes based on predetermined target segmentation points, which are obtained by processing sample scan images of multiple sample lipoprotein reagents. By using a subset of lipoprotein reagents with more distinct features as sample lipoprotein reagents, statistical analysis is performed on the reagent image to ultimately determine the segmentation points. These determined segmentation points are then applied to the reagent scan images of the lipoprotein reagents to be segmented, enabling accurate and automated batch segmentation of lipoprotein subtypes.

[0078] Based on the above embodiment, optionally, the device further includes a target segmentation point determination module 320, including:

[0079] a sample image segmentation unit, configured to obtain sample scan images of a plurality of sample lipoprotein reagents and process the sample scan images to obtain a single sample image;

[0080] The segmentation point determination unit is used to determine the target segmentation point corresponding to the single sample image according to the gray value waveform image of the single sample image.

[0081] Based on the above embodiment, optionally, the segmentation point determination unit is specifically configured to:

[0082] Determine a single demarcation point corresponding to a single sample image according to a gray value waveform image of the single sample image;

[0083] Based on each individual dividing point, the target segmentation point is determined.

[0084] Based on the above embodiment, optionally, the segmentation point determination unit is specifically configured to:

[0085] The grayscale waveform image is divided into waveforms, and the connection points of adjacent waveforms are used as single dividing points.

[0086] Based on the above embodiment, optionally, the segmentation point determination unit is specifically configured to:

[0087] Conduct statistical verification on each individual cut-off point and determine the confidence interval of each individual cut-off point;

[0088] Determine the component splitting ratio of each individual dividing point based on the confidence interval of each individual dividing point;

[0089] Determine the target segmentation point corresponding to a single dividing point according to the component segmentation ratio.

[0090] Based on the above embodiment, optionally, the segmentation point determination unit is specifically configured to:

[0091] The middle value of the confidence interval is used as the component split ratio of a single dividing point.

[0092] Based on the above embodiment, optionally, the segmentation point determination unit is specifically configured to:

[0093] Determine the total length of the distance from the peak of very low-density lipoprotein as the starting point to the peak of high-density lipoprotein as the end point in a single sample image;

[0094] The target segmentation point is obtained based on the component segmentation ratio and the total distance.

[0095] The lipoprotein subtype component classification device provided in the embodiment of the present invention can execute the lipoprotein subtype component classification method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0096] Example 4

[0097] Figure 41 is a structural diagram of an electronic device provided in Embodiment 4 of the present invention. The electronic device 10 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0098] like Figure 4 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., which is communicatively connected to the at least one processor 11. The memory stores a computer program that can be executed by the at least one processor. The processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 into the random access memory (RAM) 13. Various programs and data required for the operation of the electronic device 10 can also be stored in the RAM 13. The processor 11, ROM 12, and RAM 13 are connected to each other via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0099] Multiple components in the electronic device 10 are connected to the I / O interface 15, including an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a magnetic disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0100] The processor 11 can be any general-purpose and / or specialized processing component with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various processors for running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the method for classifying lipoprotein subtypes.

[0101] In some embodiments, the lipoprotein subtype component classification method can be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the lipoprotein subtype component classification method described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to execute the lipoprotein subtype component classification method in any other appropriate manner (e.g., by means of firmware).

[0102] Various embodiments of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0103] Computer programs for implementing the lipoprotein subtype classification methods of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The computer programs can be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0104] Example 5

[0105] The fifth embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to cause a processor to execute a method for classifying lipoprotein subtype components, the method comprising:

[0106] acquiring a reagent scanning image of the reagent to be divided into lipoproteins;

[0107] The reagent scanning image is divided into low-density lipoprotein subtype components based on predetermined target segmentation points, and the target segmentation points are obtained based on sample scanning images of multiple sample lipoprotein reagents.

[0108] In the context of the present invention, computer-readable storage media can be tangible media that can contain or store a computer program for use with an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. Computer-readable storage media can include but are not limited to electronic, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, computer-readable storage media can be machine-readable signal media. More specific examples of machine-readable storage media can include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0109] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0110] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0111] A computing system may include clients and servers. The clients and servers are typically remote from each other and typically interact via a communication network. This client-server relationship arises through computer programs running on the respective computers, creating a client-server relationship. The server may be a cloud server, also known as a cloud computing server or cloud host. This server is a hosting product within the cloud computing service ecosystem that addresses the management difficulties and limited scalability of traditional physical hosting and VPS services.

[0112] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in the present invention can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved. This is not limited herein.

[0113] The above specific embodiments do not limit the scope of protection of the present invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention are intended to be included within the scope of protection of the present invention.

Claims

1. A method for classifying lipoprotein subtypes, characterized in that: include: acquiring a reagent scanning image of the reagent to be divided into lipoproteins; dividing the reagent scan image into low-density lipoprotein subtype components based on predetermined target segmentation points, wherein the target segmentation points are obtained by processing sample scan images of multiple sample lipoprotein reagents; The method further comprises: Acquiring sample scan images of a plurality of sample lipoprotein reagents, and processing the sample scan images to obtain a single sample image; Determining a single demarcation point corresponding to the single sample image according to the gray value waveform image of the single sample image; Statistically verifying each of the individual demarcation points to determine a confidence interval for each of the individual demarcation points; wherein each of the individual demarcation points conforms to a normal distribution after Boxcox transformation; Determining the component segmentation ratio of the single demarcation point based on the confidence interval of each of the single demarcation points; The target segmentation point corresponding to the single dividing point is determined according to the component segmentation ratio.

2. The method according to claim 1, characterized in that The determining of a single demarcation point corresponding to the single sample image according to the gray value waveform image of the single sample image includes: The grayscale value waveform image is divided into waveforms, and the connection points of adjacent waveforms are used as the single dividing points.

3. The method according to claim 1, characterized in that The determining of the component segmentation ratio of the single demarcation point based on the confidence interval of each of the single demarcation points includes: The middle value of the confidence interval is used as the component division ratio of the single dividing point.

4. The method according to claim 1, wherein The step of determining the target segmentation point corresponding to the single demarcation point according to the component segmentation ratio includes: determining the total length of the distance from the peak of very low density lipoprotein as the starting point to the peak of high density lipoprotein as the end point in the single sample image; The target segmentation point is obtained based on the component segmentation ratio and the total distance.

5. A device for classifying lipoprotein subtypes, characterized in that: include: A reagent image acquisition module, used for acquiring a reagent scanning image of the reagent to be divided into lipoproteins; a reagent image segmentation module for segmenting the reagent scan image into low-density lipoprotein subtype components based on predetermined target segmentation points, wherein the target segmentation points are obtained by processing sample scan images of multiple sample lipoprotein reagents; The device further includes a target segmentation point determination module. The target segmentation point determination module includes: a sample image segmentation unit, configured to obtain sample scan images of a plurality of sample lipoprotein reagents and process the sample scan images to obtain a single sample image; A segmentation point determination unit is used to determine a single demarcation point corresponding to the single sample image based on the grayscale value waveform image of the single sample image; perform statistical verification on each of the single demarcation points to determine a confidence interval for each of the single demarcation points; wherein each of the single demarcation points conforms to a normal distribution after Boxcox transformation; determine a component segmentation ratio for the single demarcation point based on the confidence interval for each of the single demarcation points; and determine a target segmentation point corresponding to the single demarcation point based on the component segmentation ratio.

6. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to perform the method for classifying lipoprotein subtype components according to any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the method for classifying lipoprotein subtype components according to any one of claims 1 to 4 when executed.

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