WO2024016814A1 - 一种脂蛋白亚型组分划分方法、装置、设备及存储介质 - Google Patents

一种脂蛋白亚型组分划分方法、装置、设备及存储介质 Download PDF

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WO2024016814A1
WO2024016814A1 PCT/CN2023/095007 CN2023095007W WO2024016814A1 WO 2024016814 A1 WO2024016814 A1 WO 2024016814A1 CN 2023095007 W CN2023095007 W CN 2023095007W WO 2024016814 A1 WO2024016814 A1 WO 2024016814A1
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lipoprotein
image
point
reagent
sample
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PCT/CN2023/095007
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English (en)
French (fr)
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孙林香
张心觉
楼敬伟
林灵
吴守信
汪梦竹
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上海宝藤生物医药科技股份有限公司
上海宝藤医学检验所有限公司
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Publication of WO2024016814A1 publication Critical patent/WO2024016814A1/zh

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0012—Biomedical image inspection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/20—Image preprocessing
    • G06V10/26—Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/764—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/60—Type of objects
    • G06V20/69—Microscopic objects, e.g. biological cells or cellular parts
    • G06V20/695—Preprocessing, e.g. image segmentation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/60—Type of objects
    • G06V20/69—Microscopic objects, e.g. biological cells or cellular parts
    • G06V20/698—Matching; Classification
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10056—Microscopic image
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing

Definitions

  • the present application relates to the technical field of lipoprotein subtype component classification, for example, to a lipoprotein subtype component classification method, device, equipment and storage medium.
  • LDL Low-density lipoprotein
  • LDL classes 1 and 2 are considered "normal LDL” and are responsible for normal cholesterol transport.
  • LDL categories 3 to 7 are considered “bad or abnormal LDL” and are susceptible to oxidation leading to cardiovascular disease.
  • This application provides a method, device, equipment and storage medium for classifying lipoprotein subtype components to avoid inaccurate classification of LDL subcomponents.
  • a method for classifying lipoprotein subtype components including:
  • the reagent scanned image is divided into low-density lipoprotein subtype components based on predetermined target segmentation points, which are obtained based on sample scanned images of multiple sample lipoprotein reagents.
  • a device for classifying lipoprotein subtype components including:
  • the reagent image acquisition module is configured to acquire the reagent scan image of the lipoprotein reagent to be divided;
  • the reagent image division module is configured to divide the reagent scan image into low-density lipoprotein subtype components based on a predetermined target segmentation point, and the target segmentation point is obtained based on sample scan image processing of multiple sample lipoprotein reagents.
  • an electronic device includes:
  • a memory communicatively connected to at least one processor; wherein,
  • the memory stores a computer program that can be executed by at least one processor, and the computer program is executed by at least one processor, so that at least one processor can execute the method for classifying lipoprotein subtype components according to any embodiment of the present application.
  • a computer-readable storage medium stores computer instructions.
  • the computer instructions are used to enable the processor to implement the lipoprotein subtype group of any embodiment of the present application when executed. Partitioning method.
  • Figure 1 is a flow chart of a method for classifying lipoprotein subtype components provided by an embodiment of the present application
  • Figure 2a is a flow chart of a method for classifying lipoprotein subtype components provided by another embodiment of the present application.
  • Figure 2b is a schematic diagram of a sample scan image and a single sample image provided by an embodiment of the present application
  • Figure 2c is a schematic diagram of segmentation of a gray value waveform image provided by an embodiment of the present application.
  • Figure 2d is a schematic diagram of a confidence interval of a single cut-off point provided by an embodiment of the present application
  • Figure 2e is a schematic diagram of another confidence interval of a single cut-off point provided by an embodiment of the present application.
  • Figure 3 is a schematic structural diagram of a device for classifying lipoprotein subtype components provided by an embodiment of the present application
  • FIG. 4 is a schematic structural diagram of an electronic device provided by an embodiment of the present application.
  • Figure 1 is a flow chart of a method for classifying lipoprotein subtype components provided by an embodiment of the present application. This embodiment can be applied to classifying low-density lipoprotein into subtype components. This method can be divided into lipoprotein subtypes.
  • the lipoprotein subtype component classification device can be implemented in the form of hardware and/or software, and the lipoprotein subtype component classification device can be configured in an electronic device. As shown in Figure 1, the method includes:
  • S120 Classify low-density lipoprotein subtype components on the reagent scanned image based on predetermined target segmentation points, which are obtained based on sample scanned images of multiple sample lipoprotein reagents.
  • image processing is performed in advance based on lipoprotein reagents with obvious characteristics to obtain target segmentation points for LDL subcomponent classification, that is, dividing points.
  • target segmentation points for LDL subcomponent classification that is, dividing points.
  • the predetermined target segmentation points are finally applied to the reagent scan image to achieve automated segmentation.
  • the classification of low-density lipoprotein subtype components in the reagent scan image based on the target segmentation point can refer to the method of classifying lipoprotein subtype components in related technologies, and is not limited here.
  • the technical solution of the embodiment of the present application is to obtain a reagent scan image of the lipoprotein reagent to be divided; the reagent scan image is divided into low-density lipoprotein subtype components based on a predetermined target segmentation point, and the target segmentation point is based on multiple
  • the sample scanned image of each sample lipoprotein reagent is processed.
  • statistical analysis is performed on the reagent image to finally determine the segmentation point.
  • the lipoprotein reagent can be realized on the reagent scan image of the lipoprotein reagent to be divided. Accurate and automated batch fractionation of protein isoform fractions.
  • Figure 2a is a flow chart of a method for classifying lipoprotein subtype components provided in an embodiment of the present application. Based on the above embodiment, this embodiment refines the determination of target segmentation points. As shown in Figure 2a, the method includes:
  • a small batch of lipoprotein reagents with obvious characteristics are used as sample lipoprotein reagents, and a reagent scan image of the lipoprotein reagent to be sampled after electrophoresis is obtained as a sample scan image.
  • the original scanned image is processed to obtain a single sample image, It includes: performing effective area interception and segmentation on the original scanned image to obtain multiple single sample images. For example, the reagents in the sample scan image are separated and their effective parts are intercepted, and then the image of a single reagent map is obtained as a single sample image.
  • Figure 2b is a schematic diagram of a sample scan image and a single sample image provided by an embodiment of the present application.
  • the left part of the arrow in Figure 2b is a schematic diagram of the sample scanned image, and the right part of the arrow in Figure 2b is a schematic diagram of a single scanned image.
  • the gray value waveform image can be drawn, and the target segmentation point can be determined from the gray value waveform image according to certain mathematical rules.
  • determining the target segmentation point corresponding to the single sample image based on the gray value waveform image of the single sample image includes: based on the gray value waveform of the single sample image The image determines a single dividing point corresponding to the single sample image; based on the single dividing point, the target segmentation point is determined. After generating the gray value waveform image, find a single image dividing line according to certain mathematical rules, determine a single dividing point based on the single image dividing line, and then determine the target dividing point based on the single dividing point.
  • Figure 2c is a schematic diagram of segmentation of a gray value waveform image provided by an embodiment of the present application.
  • the ordinate in the figure is the statistical gray value
  • the abscissa represents the position, which corresponds to a single sample image.
  • the two highest peaks in Figure 2c are the peak of high-density lipoprotein HDL and the peak of very low-density lipoprotein VLDL.
  • the other dotted dividing lines are multiple dividing lines found through mathematical rules.
  • the other dotted dividing lines correspond to The dividing point is a single dividing point corresponding to a single sample image.
  • determining a single dividing point corresponding to the single sample image based on the gray value waveform image of the single sample image includes: dividing the gray value waveform image into waveforms, and dividing adjacent waveforms into The connection point serves as the single dividing point. Multiple waveforms contained in a grayscale waveform image can be identified, and the connection between adjacent waveforms can be used as a single dividing point.
  • determining the target segmentation point based on the single demarcation point includes: performing statistical verification on the single demarcation point, and determining the value of the single demarcation point. Confidence interval; determine the group of the single cut-off point based on the confidence interval of the single cut-off point divide the dividing ratio; determine the target dividing point corresponding to the single dividing point according to the component dividing ratio.
  • the coordinates of multiple single dividing points of a single sample image can be searched in batches. Based on the coordinates of each single dividing point, the percentage of the single dividing point in the total length of each image in the batch can be obtained, where the total length refers to the gray value. The distance between the two highest peaks in the degree waveform image.
  • the target segmentation points corresponding to each single boundary point are determined sequentially through the above method until the target segmentation points corresponding to all single boundary points in all single sample images are obtained.
  • determining the component division ratio corresponding to a single dividing point based on the confidence interval can be: determining the characteristic value of the confidence interval based on the upper limit and lower limit of the confidence interval, and using the characteristic value of the confidence interval as the component corresponding to the single dividing point.
  • Split ratio Among them, the eigenvalue of the confidence interval can be the weighted sum of the upper limit and the lower limit.
  • Figure 2d is a schematic diagram of a confidence interval of a single cut-off point provided by an embodiment of the present application.
  • Figure 2e is a schematic diagram of another confidence interval of a single cut-off point provided by the embodiment of the present application. The confidence intervals of two single cutoff points are schematically shown in Figure 2d and Figure 2e.
  • determining the component division ratio of the single cutoff point based on the confidence interval of the single cutoff point includes: using the intermediate value of the confidence interval as the component of the single cutoff point.
  • Split ratio For example, the middle value of the confidence interval, that is, the average of the upper limit and lower limit of the confidence interval, is used as the component split ratio of a single cut-off point. As shown in Figure 2d, 36.5% can be used as the component splitting ratio corresponding to the single dividing point in Figure 2d; as shown in Figure 2e, 47.3% can be used as the component dividing ratio corresponding to the single dividing point in Figure 2e.
  • determining the target segmentation point corresponding to the single demarcation point based on the component segmentation ratio includes: determining in the single sample image, in the form of very low-density lipoprotein The total distance between the wave peak as the starting point and the high-density lipoprotein wave peak as the end point; the target segmentation point is obtained based on the component segmentation ratio and the total distance. For example, take the peak of VLDL in each waveform as the starting point, the peak of HDL as the ending point, and the distance between the two points as the total length.
  • the distance of the corresponding dividing point relative to the starting point plus the coordinates of the starting point, which is the coordinates of the dividing point, that is, the coordinates of the target dividing point, and the remaining single dividing points
  • the target segmentation point corresponding to the position can be calculated using the same method as above.
  • the technical solution of this embodiment is to acquire sample scan images of multiple sample lipoprotein reagents, process the sample scan images to obtain a single sample image; determine the single sample based on the gray value waveform image of the single sample image.
  • the lipoprotein reagent's reagent scan image enables accurate and automated batch segmentation of lipoprotein subtype components.
  • FIG 3 is a schematic structural diagram of a device for classifying lipoprotein subtype components provided in an embodiment of the present application. As shown in Figure 3, the device includes:
  • the reagent image acquisition module 310 is configured to acquire the reagent scan image of the lipoprotein reagent to be divided;
  • the reagent image dividing module 320 is configured to divide the reagent scanned image into low-density lipoprotein subtype components based on predetermined target segmentation points, which are obtained based on sample scanned images of multiple sample lipoprotein reagents.
  • the technical solution of this embodiment is to obtain a reagent scan image of the lipoprotein reagent to be divided; the reagent scan image is divided into low-density lipoprotein subtype components based on a predetermined target segmentation point, and the target segmentation point is based on multiple
  • the sample scanned image of the sample lipoprotein reagent is processed.
  • statistical analysis is performed on the reagent image to finally determine the segmentation point.
  • the lipoprotein reagent can be realized on the reagent scan image of the lipoprotein reagent to be divided. Accurate and automated batch fractionation of protein isoform fractions.
  • the device further includes a target segmentation point determination module 320, including:
  • a sample image segmentation unit is configured to obtain sample scan images of multiple sample lipoprotein reagents, and process the sample scan images to obtain a single sample image;
  • the segmentation point determination unit is configured to determine the target segmentation point corresponding to the single sample image based on the gray value waveform image of the single sample image.
  • the split point determination unit is set to:
  • the target segmentation point is determined.
  • the split point determination unit is set to:
  • the gray value waveform image is divided into waveforms, and the connection points of adjacent waveforms are used as a single dividing point.
  • the split point determination unit is set to:
  • the target segmentation point corresponding to a single boundary point is determined based on the component segmentation ratio.
  • the split point determination unit is set to:
  • the middle value of the confidence interval is used as the component split ratio at a single cutoff point.
  • the split point determination unit is set to:
  • the target segmentation point is obtained based on the component segmentation ratio and the total distance.
  • the lipoprotein subtype component classification device provided in the embodiments of this application can execute the lipoprotein subtype component classification method provided in any embodiment of this application, and has functional modules and beneficial effects corresponding to the execution method.
  • FIG. 4 is a schematic structural diagram of an electronic device provided by an embodiment of the present application.
  • Electronic device 10 is intended to represent many forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers.
  • Electronic devices may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices (eg, helmets, glasses, watches, etc.), and other similar computing devices.
  • the components shown herein, their connections and relationships, and their functions are examples only and are not intended to limit the implementation of the present application as described and/or claimed herein.
  • the electronic device 10 includes at least one processor 11, and a memory communicatively connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores There is a computer program that can be executed by at least one processor.
  • the processor 11 can perform the operation according to the computer program stored in the read-only memory (ROM) 12 or loaded from the storage unit 18 into the random access memory (RAM) 13. Performs a variety of appropriate actions and processes.
  • RAM 13 various programs and data required for the operation of the electronic device 10 can also be stored.
  • the processor 11, the ROM 12 and the RAM 13 are connected to each other via the bus 14.
  • An input/output (I/O) interface 15 is also connected to bus 14 .
  • the I/O interface 15 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. etc.; and communication unit 19, such as network card, modem, wireless communication transceiver, etc.
  • the communication unit 19 allows the electronic device 10 to exchange information/data with other devices through a computer network such as the Internet and/or various telecommunications networks.
  • Processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. deal with Some examples of processors 11 include, but are not limited to, central processing units (CPUs), graphics processing units (GPUs), various specialized artificial intelligence (AI) computing chips, various processors that run machine learning model algorithms, digital signal processors (DSP), and any appropriate processor, controller, microcontroller, etc.
  • the processor 11 performs a plurality of methods and processes described above, such as lipoprotein subtype component partitioning methods.
  • the lipoprotein subtype component classification method can be implemented as a computer program, which is tangibly included in a computer-readable storage medium, such as the storage unit 18.
  • part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19.
  • the processor 11 may be configured to perform the lipoprotein subtype fractionation method in any other suitable manner (eg, by means of firmware).
  • Various implementations of the systems and techniques described above may 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), systems on a chip implemented in a system (SOC), load programmable logic device (CPLD), computer hardware, firmware, software, and/or a combination thereof.
  • FPGAs field programmable gate arrays
  • ASICs application specific integrated circuits
  • ASSPs application specific standard products
  • SOC system
  • CPLD load programmable logic device
  • computer hardware firmware, software, and/or a combination thereof.
  • These various embodiments may include implementation in one or more computer programs executable and/or interpreted on a programmable system including at least one programmable processor, the programmable processor
  • the processor which may be a special purpose or general purpose programmable processor, may 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.
  • An output device may be a special purpose or general purpose programmable processor, may 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.
  • An output device may be a special purpose or general purpose programmable processor, may 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.
  • the computer program used to implement the lipoprotein subtype component partitioning method of the present application can be written using any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that the computer program, when executed by the processor, causes the functions/operations specified in the flowcharts and/or block diagrams to be implemented. A computer program may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
  • Embodiments of the present application also provide a computer-readable storage medium.
  • the computer-readable storage medium stores computer instructions.
  • the computer instructions are used to cause the processor to execute a method for classifying lipoprotein subtype components.
  • the method includes:
  • the reagent scanned image is divided into low-density lipoprotein subtype components based on predetermined target segmentation points, which are obtained based on sample scanned images of multiple sample lipoprotein reagents.
  • a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in connection with an instruction execution system, apparatus, or device.
  • Computer-readable storage media may include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or devices, or any suitable combination of the foregoing.
  • the computer-readable storage medium may be a machine-readable signal medium.
  • machine-readable storage media would include one or more wire-based electrical connections, laptop disks, hard drives, random access memory (RAM), read only memory (ROM), erasable programmable read only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination of the above.
  • the computer-readable storage medium may be a non-transitory computer-readable storage medium.
  • the systems and techniques described herein may be implemented on an electronic device having a display device (eg, a CRT (cathode ray tube) or LCD (liquid crystal display)) for displaying information to the user monitor); and a keyboard and pointing device (e.g., a mouse or a trackball) through which a user can provide input to the electronic device.
  • a display device eg, a CRT (cathode ray tube) or LCD (liquid crystal display)
  • a keyboard and pointing device e.g., a mouse or a trackball
  • Other kinds of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and may be provided in any form, including Acoustic input, voice input or tactile input) to receive input from the user.
  • the systems and techniques described herein may 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's computer having a graphical user interface or web browser through which the user can interact with implementations of the systems and technologies described herein), or including such backend components, middleware components, or any combination of front-end components in a computing system.
  • the components of the system may be interconnected by any form or medium of digital data communication (eg, a communications network). Examples of communication networks include: local area network (LAN), wide area network (WAN), blockchain network, and the Internet.
  • Computing systems may include clients and servers.
  • Clients and servers are generally remote from each other and typically interact over a communications network.
  • the relationship of client and server is created by computer programs running on corresponding computers and having a client-server relationship with each other.
  • the server can be a cloud server, also known as cloud computing server or cloud host. It is a host product in the cloud computing service system to avoid the difficult management and weak business scalability of physical hosts and VPS services in related technologies. Case.

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Abstract

本申请公开了一种脂蛋白亚型组分划分方法、装置、设备及存储介质。方法包括：获取待划分脂蛋白试剂的试剂扫描图像(S110)；基于预先确定的目标分割点位对所述试剂扫描图像进行低密度脂蛋白亚型组分的划分，所述目标分割点位基于多个样本脂蛋白试剂的样本扫描图像处理得到(S120)。

Description

一种脂蛋白亚型组分划分方法、装置、设备及存储介质
本申请要求在2022年7月20日提交中国专利局、申请号为202210863495.5的中国专利申请的优先权，该申请的全部内容通过引用结合在本申请中。
技术领域
本申请涉及脂蛋白亚型组分划分技术领域，例如涉及一种脂蛋白亚型组分划分方法、装置、设备及存储介质。
背景技术
低密度脂蛋白(low-density lipoprotein，LDL)是人体内胆固醇运输的主要载体，可以细分为七种类型；但是LDL含量超标会引起发炎反应，氧化态的低密度脂蛋白被巨噬细胞吞噬后，会在血管中形成脂肪斑，导致血脂异常或者动脉硬化。其中“越小”的LDL越容易被氧化，在血管壁形成斑块。LDL的分类1和2(大的LDL)被认为是“正常的LDL”，它们负责正常的胆固醇运输。LDL的分类3到7(小的LDL)被认为是“不好的或非正常的LDL”，它们容易被氧化从而导致心血管疾病的产生。“小的”低密度脂蛋白含量与患冠状动脉疾病的关系现在已经得到证实。单一测定总的LDL含量并不能显示患心血管病的风险，因为它不能区分“大的”LDL和“小的”LDL。即使总LDL水平显示正常，但其中小而密(3-7)型别超标，可使患心血管疾病的风险达到3倍以上。在早期发现患病几率，可以大幅度的防治心血管疾病的发生。
LDL的亚组分分类在世界范围内尚无一个完整的标准，导致LDL的亚组分分类不准确。
发明内容
本申请提供了一种脂蛋白亚型组分划分方法、装置、设备及存储介质，以避免LDL的亚组分分类不准确的情况。
根据本申请的一方面，提供了一种脂蛋白亚型组分划分方法，包括：
获取待划分脂蛋白试剂的试剂扫描图像；
基于预先确定的目标分割点位对试剂扫描图像进行低密度脂蛋白亚型组分的划分，目标分割点位基于多个样本脂蛋白试剂的样本扫描图像处理得到。
根据本申请的另一方面，提供了一种脂蛋白亚型组分划分装置，包括：
试剂图像获取模块，设置为获取待划分脂蛋白试剂的试剂扫描图像；
试剂图像划分模块，设置为基于预先确定的目标分割点位对试剂扫描图像进行低密度脂蛋白亚型组分的划分，目标分割点位基于多个样本脂蛋白试剂的样本扫描图像处理得到。
根据本申请的另一方面，提供了一种电子设备，电子设备包括：
至少一个处理器；以及
与至少一个处理器通信连接的存储器；其中，
存储器存储有可被至少一个处理器执行的计算机程序，计算机程序被至少一个处理器执行，以使至少一个处理器能够执行本申请任一实施例的脂蛋白亚型组分划分方法。
根据本申请的另一方面，提供了一种计算机可读存储介质，计算机可读存储介质存储有计算机指令，计算机指令用于使处理器执行时实现本申请任一实施例的脂蛋白亚型组分划分方法。
附图说明
图1是本申请一实施例提供的一种脂蛋白亚型组分划分方法的流程图；
图2a是本申请另一实施例提供的一种脂蛋白亚型组分划分方法的流程图；
图2b是本申请一实施例提供的一种样本扫描图像和单个样本图像的示意图；
图2c是本申请一实施例提供的一种灰度值波形图像的分割示意图；
图2d是本申请一实施例提供的一种单个分界点位的置信区间的示意图；
图2e是本申请一实施例提供的另一种单个分界点位的置信区间的示意图；
图3是本申请一实施例提供的一种脂蛋白亚型组分划分装置的结构示意图；
图4是本申请一实施例提供的一种电子设备的结构示意图。
具体实施方式
需要说明的是，本申请的说明书和权利要求书及上述附图中的术语“第一”、“第二”等是用于区别类似的对象，而不必用于描述特定的顺序或先后次序。应该理解这样使用的数据在适当情况下可以互换，以便这里描述的本申请的实施例能够以除了在这里图示或描述的那些以外的顺序实施。此外，术语“包括”和“具有”以及他们的任何变形，意图在于覆盖不排他的包含，例如，包含了一系列步骤或单元的过程、方法、系统、产品或设备不必限于清楚地列出的那些步骤或单元，而是可包括没有清楚地列出的或对于这些过程、方法、产品或设备固有 的其它步骤或单元。
图1是本申请实施例提供的一种脂蛋白亚型组分划分方法的流程图，本实施例可适用于对低密度脂蛋白进行亚型组分划分情况，该方法可以由脂蛋白亚型组分划分装置来执行，该脂蛋白亚型组分划分装置可以采用硬件和/或软件的形式实现，该脂蛋白亚型组分划分装置可配置于电子设备中。如图1所示，该方法包括：
S110、获取待划分脂蛋白试剂的试剂扫描图像。
S120、基于预先确定的目标分割点位对所述试剂扫描图像进行低密度脂蛋白亚型组分的划分，所述目标分割点位基于多个样本脂蛋白试剂的样本扫描图像处理得到。
本申请实施例中，预先基于具有明显特征的脂蛋白试剂进行图像处理，得到LDL的亚组分分类的目标分割点位即划分点。在进行脂蛋白亚型组分的划分时，最后将预先确定的目标分割点位应用在试剂扫描图像上从而实现自动化分割。其中，基于目标分割点位对试剂扫描图像的低密度脂蛋白亚型组分的划分可以参考相关技术中脂蛋白亚型组分划分的方法，在此不做限制。
例如，可以获取电泳后待划分脂蛋白试剂的试剂扫描图像，统计试剂扫描图像中的灰度值得到试剂扫描图像的试剂灰度值统计图，基于目标分割点位对试剂灰度值统计图进行划分。
本申请实施例的技术方案，通过获取待划分脂蛋白试剂的试剂扫描图像；基于预先确定的目标分割点位对试剂扫描图像进行低密度脂蛋白亚型组分的划分，目标分割点位基于多个样本脂蛋白试剂的样本扫描图像处理得到。通过使用一部分特征较明显的脂蛋白试剂作为样本脂蛋白试剂，在试剂图像上进行统计分析最终确定分割点位，基于确定的分割点位应用在待划分脂蛋白试剂的试剂扫描图像上能够实现脂蛋白亚型组分的准确及自动化批量分割。
图2a是本申请实施例提供的一种脂蛋白亚型组分划分方法的流程图。本实施例在上述实施例的基础上，对目标分割点位的确定进行了细化，如图2a所示，该方法包括：
S210、获取多个样本脂蛋白试剂的样本扫描图像，对所述样本扫描图像进行处理得到单个样本图像。
在本实施例中，使用一小批具有明显特征的脂蛋白试剂作为样本脂蛋白试剂，获取电泳后待样本脂蛋白试剂的试剂扫描图像作为样本扫描图像。
一个实现方式中，所述对所述原始扫描图像进行处理得到单个样本图像， 包括：对所述原始扫描图像进行有效区域截取分割，得到多个所述单个样本图像。例如，将样本扫描图像中的试剂进行分离并截取其有效部分，之后得到单个试剂图的图像作为单个样本图像。
图2b是本申请实施例提供的一种样本扫描图像和单个样本图像的示意图。图2b中箭头左侧部分为样本扫描图像的示意图，图2b中箭头右侧部分为单个扫描图像的示意图。
S220、根据所述单个样本图像的灰度值波形图像确定所述单个样本图像对应的目标分割点位。
在本实施例中，通过对截取的单个样本图像进行灰度值分析，可以绘制灰度值波形图像，并按照一定的数学规则从灰度值波形图像中确定目标分割点位。
整体来说，通过使用数学规则来在灰度值波形图像上找到低密度脂蛋白(LDL)的每个组分的分割点位，并采用极低密度脂蛋白VLDL和高密度脂蛋白HDL的波峰作为起始点和终止点来计算百分比，然后对该批次每个分割点位的数据进行统计分析，并计算其置信区间，基于置信区间确定目标分割点位。
在本申请的一种实施方式中，所述根据所述单个样本图像的灰度值波形图像确定所述单个样本图像对应的目标分割点位，包括：根据所述单个样本图像的灰度值波形图像确定所述单个样本图像对应的单个分界点位；基于所述单个分界点位，确定所述目标分割点位。生成灰度值波形图像后，按照一定的数学规则找出单个图像分割线，基于单个图像分割线确定单个分界点位，然后根据单个分界点位确定目标分割点位。
图2c是本申请实施例提供的一种灰度值波形图像的分割示意图。如图2c所示，图中纵坐标为统计的灰度值，横坐标表示位置，与单个样本图像相对应。图2c中两个最高的波峰依次为高密度脂蛋白HDL的波峰和极低密度脂蛋白VLDL的波峰，其余虚线分割线为是通过数学规则查找出的多个分界线，其余虚线分割线对应的分界点位即为单个样本图像对应的单个分界点位。
一个实现方式中，所述根据所述单个样本图像的灰度值波形图像确定所述单个样本图像对应的单个分界点位，包括：对所述灰度值波形图像进行波形划分，将相邻波形的连接点位作为所述单个分界点位。可以识别灰度值波形图像中包含的多个波形，将相邻波形的连接处作为单个分界点位。
在本申请的一种实施方式中，所述基于所述单个分界点位，确定所述目标分割点位，包括：对所述单个分界点位进行统计学验证，确定所述单个分界点位的置信区间；基于所述单个分界点位的置信区间确定所述单个分界点位的组 分分割比例；根据所述组分分割比例确定所述单个分界点位对应的目标分割点位。可以批量查找单个样本图像的多个单个分界点位的坐标，基于每个单个分界点位的坐标得到该批次中每张图像中该单个分界点位占总长的百分比，其中总长是指以灰度值波形图像中两个最高的波峰之间的距离。然后对统计的百分比点位进行统计学验证，所有点位在经过boxcox变换后均符合正态分布，并在此基础上计算每个单个分界点位的置信区间。然后基于置信区间确定该单个分界点位对应的组分分割比例，最终基于组分分割比例确定该单个分界点位对应的目标分割点位。通过上述方法依次确定每个单个分界点位对应的目标分割点位，直到得到所有单个样本图像中所有单个分界点位对应的目标分割点位。
其中，基于置信区间确定单个分界点位对应的组分分割比例可以为：基于置信区间的上极限和下极限确定置信区间的特征值，将置信区间的特征值作为单个分界点位对应的组分分割比例。其中，置信区间的特征值可以为上极限和下极限的加权求和值。
图2d是本申请实施例提供的一种单个分界点位的置信区间的示意图。图2e是本申请实施例提供的另一种单个分界点位的置信区间的示意图。图2d和图2e中示意性的示出了其中两个单个分界点位的置信区间。
一个实现方式中，所述基于所述单个分界点位的置信区间确定所述单个分界点位的组分分割比例，包括：将所述置信区间的中间值作为所述单个分界点位的组分分割比例。例如，将置信区间的中间值，即置信区间的上极限和下极限的平均值作为单个分界点位的组分分割比例。以图2d所示，可以将36.5％作为图2d中单个分界点位对应的组分分割比例；以图2e所示，可以将47.3％作为图2e中单个分界点位对应的组分分割比例。
在本申请的一种实施方式中，所述根据所述组分分割比例确定所述单个分界点位对应的目标分割点位，包括：确定所述单个样本图像中，以极低密度脂蛋白的波峰为起点，以高密度脂蛋白的波峰为终点的距离总长；基于所述组分分割比例和所述距离总长得到所述目标分割点位。例如，以每个波形图中VLDL的波峰作为起点，HDL的波峰作为终止点，两点之间的距离设为总长。通过上述示例中的两个百分比点位乘以总长可以获得对应分割点相对余起点的距离，再加上起点的坐标，即为分割点的坐标，即目标分割点位的坐标，其余单个分界点位对应的目标分割点位可以采用上述相同的方法计算得到。
S230、获取待划分脂蛋白试剂的试剂扫描图像。
S240、基于预先确定的目标分割点位对所述试剂扫描图像进行低密度脂蛋 白亚型组分的划分。
本实施例的技术方案，通过获取多个样本脂蛋白试剂的样本扫描图像，对所述样本扫描图像进行处理得到单个样本图像；根据所述单个样本图像的灰度值波形图像确定所述单个样本图像对应的目标分割点位；通过使用一部分特征较明显的脂蛋白试剂作为样本脂蛋白试剂，在试剂图像上进行统计分析最终确定目标分割点位，使得基于确定的目标分割点位应用在待划分脂蛋白试剂的试剂扫描图像上能够实现脂蛋白亚型组分的准确及自动化批量分割。
图3是本申请实施例提供的一种脂蛋白亚型组分划分装置的结构示意图。如图3所示，该装置包括：
试剂图像获取模块310，设置为获取待划分脂蛋白试剂的试剂扫描图像；
试剂图像划分模块320，设置为基于预先确定的目标分割点位对试剂扫描图像进行低密度脂蛋白亚型组分的划分，目标分割点位基于多个样本脂蛋白试剂的样本扫描图像处理得到。
本实施例的技术方案，通过获取待划分脂蛋白试剂的试剂扫描图像；基于预先确定的目标分割点位对试剂扫描图像进行低密度脂蛋白亚型组分的划分，目标分割点位基于多个样本脂蛋白试剂的样本扫描图像处理得到。通过使用一部分特征较明显的脂蛋白试剂作为样本脂蛋白试剂，在试剂图像上进行统计分析最终确定分割点位，基于确定的分割点位应用在待划分脂蛋白试剂的试剂扫描图像上能够实现脂蛋白亚型组分的准确及自动化批量分割。
例如，装置还包括目标分割点位确定模块320，包括：
样本图像分割单元，设置为获取多个样本脂蛋白试剂的样本扫描图像，对样本扫描图像进行处理得到单个样本图像；
分割点位确定单元，设置为根据单个样本图像的灰度值波形图像确定单个样本图像对应的目标分割点位。
例如，分割点位确定单元设置为：
根据单个样本图像的灰度值波形图像确定单个样本图像对应的单个分界点位；
基于单个分界点位，确定目标分割点位。
例如，分割点位确定单元设置为：
对灰度值波形图像进行波形划分，将相邻波形的连接点位作为单个分界点位。
例如，分割点位确定单元设置为：
对单个分界点位进行统计学验证，确定单个分界点位的置信区间；
基于单个分界点位的置信区间确定单个分界点位的组分分割比例；
根据组分分割比例确定单个分界点位对应的目标分割点位。
例如，分割点位确定单元设置为：
将置信区间的中间值作为单个分界点位的组分分割比例。
例如，分割点位确定单元设置为：
确定单个样本图像中，以极低密度脂蛋白的波峰为起点，以高密度脂蛋白的波峰为终点的距离总长；
基于组分分割比例和距离总长得到目标分割点位。
本申请实施例所提供的脂蛋白亚型组分划分装置可执行本申请任意实施例所提供的脂蛋白亚型组分划分方法，具备执行方法相应的功能模块和有益效果。
图4是本申请实施例提供的一种电子设备的结构示意图。电子设备10旨在表示多种形式的数字计算机，诸如，膝上型计算机、台式计算机、工作台、个人数字助理、服务器、刀片式服务器、大型计算机、和其它适合的计算机。电子设备还可以表示多种形式的移动装置，诸如，个人数字处理、蜂窝电话、智能电话、可穿戴设备(如头盔、眼镜、手表等)和其它类似的计算装置。本文所示的部件、它们的连接和关系、以及它们的功能仅仅作为示例，并且不意在限制本文中描述的和/或者要求的本申请的实现。
如图4所示，电子设备10包括至少一个处理器11，以及与至少一个处理器11通信连接的存储器，如只读存储器(ROM)12、随机访问存储器(RAM)13等，其中，存储器存储有可被至少一个处理器执行的计算机程序，处理器11可以根据存储在只读存储器(ROM)12中的计算机程序或者从存储单元18加载到随机访问存储器(RAM)13中的计算机程序，来执行多种适当的动作和处理。在RAM 13中，还可存储电子设备10操作所需的多种程序和数据。处理器11、ROM 12以及RAM 13通过总线14彼此相连。输入/输出(I/O)接口15也连接至总线14。
电子设备10中的多个部件连接至I/O接口15，包括：输入单元16，例如键盘、鼠标等；输出单元17，例如多种类型的显示器、扬声器等；存储单元18，例如磁盘、光盘等；以及通信单元19，例如网卡、调制解调器、无线通信收发机等。通信单元19允许电子设备10通过诸如因特网的计算机网络和/或多种电信网络与其他设备交换信息/数据。
处理器11可以是多种具有处理和计算能力的通用和/或专用处理组件。处理 器11的一些示例包括但不限于中央处理单元(CPU)、图形处理单元(GPU)、多种专用的人工智能(AI)计算芯片、多种运行机器学习模型算法的处理器、数字信号处理器(DSP)、以及任何适当的处理器、控制器、微控制器等。处理器11执行上文所描述的多个方法和处理，例如脂蛋白亚型组分划分方法。
在一些实施例中，脂蛋白亚型组分划分方法可被实现为计算机程序，其被有形地包含于计算机可读存储介质，例如存储单元18。在一些实施例中，计算机程序的部分或者全部可以经由ROM 12和/或通信单元19而被载入和/或安装到电子设备10上。当计算机程序加载到RAM 13并由处理器11执行时，可以执行上文描述的脂蛋白亚型组分划分方法的一个或多个步骤。备选地，在其他实施例中，处理器11可以通过其他任何适当的方式(例如，借助于固件)而被配置为执行脂蛋白亚型组分划分方法。
本文中以上描述的系统和技术的多种实施方式可以在数字电子电路系统、集成电路系统、场可编程门阵列(FPGA)、专用集成电路(ASIC)、专用标准产品(ASSP)、芯片上系统的系统(SOC)、负载可编程逻辑设备(CPLD)、计算机硬件、固件、软件、和/或它们的组合中实现。这些多种实施方式可以包括：实施在一个或者多个计算机程序中，该一个或者多个计算机程序可在包括至少一个可编程处理器的可编程系统上执行和/或解释，该可编程处理器可以是专用或者通用可编程处理器，可以从存储系统、至少一个输入装置、和至少一个输出装置接收数据和指令，并且将数据和指令传输至该存储系统、该至少一个输入装置、和该至少一个输出装置。
用于实施本申请的脂蛋白亚型组分划分方法的计算机程序可以采用一个或多个编程语言的任何组合来编写。这些计算机程序可以提供给通用计算机、专用计算机或其他可编程数据处理装置的处理器，使得计算机程序当由处理器执行时使流程图和/或框图中所规定的功能/操作被实施。计算机程序可以完全在机器上执行、部分地在机器上执行，作为独立软件包部分地在机器上执行且部分地在远程机器上执行或完全在远程机器或服务器上执行。
本申请实施例还提供了一种计算机可读存储介质，计算机可读存储介质存储有计算机指令，计算机指令用于使处理器执行一种脂蛋白亚型组分划分方法，该方法包括：
获取待划分脂蛋白试剂的试剂扫描图像；
基于预先确定的目标分割点位对试剂扫描图像进行低密度脂蛋白亚型组分的划分，目标分割点位基于多个样本脂蛋白试剂的样本扫描图像处理得到。
在本申请的上下文中，计算机可读存储介质可以是有形的介质，其可以包含或存储以供指令执行系统、装置或设备使用或与指令执行系统、装置或设备结合地使用的计算机程序。计算机可读存储介质可以包括但不限于电子的、磁性的、光学的、电磁的、红外的、或半导体系统、装置或设备，或者上述内容的任何合适组合。备选地，计算机可读存储介质可以是机器可读信号介质。机器可读存储介质的更具体示例会包括基于一个或多个线的电气连接、便携式计算机盘、硬盘、随机存取存储器(RAM)、只读存储器(ROM)、可擦除可编程只读存储器(EPROM或快闪存储器)、光纤、便捷式紧凑盘只读存储器(CD-ROM)、光学储存设备、磁储存设备、或上述内容的任何合适组合。计算机可读存储介质可以为非暂态计算机可读存储介质。
为了提供与用户的交互，可以在电子设备上实施此处描述的系统和技术，该电子设备具有：用于向用户显示信息的显示装置(例如，CRT(阴极射线管)或者LCD(液晶显示器)监视器)；以及键盘和指向装置(例如，鼠标或者轨迹球)，用户可以通过该键盘和该指向装置来将输入提供给电子设备。其它种类的装置还可以用于提供与用户的交互；例如，提供给用户的反馈可以是任何形式的传感反馈(例如，视觉反馈、听觉反馈、或者触觉反馈)；并且可以用任何形式(包括声输入、语音输入或者、触觉输入)来接收来自用户的输入。
可以将此处描述的系统和技术实施在包括后台部件的计算系统(例如，作为数据服务器)、或者包括中间件部件的计算系统(例如，应用服务器)、或者包括前端部件的计算系统(例如，具有图形用户界面或者网络浏览器的用户计算机，用户可以通过该图形用户界面或者该网络浏览器来与此处描述的系统和技术的实施方式交互)、或者包括这种后台部件、中间件部件、或者前端部件的任何组合的计算系统中。可以通过任何形式或者介质的数字数据通信(例如，通信网络)来将系统的部件相互连接。通信网络的示例包括：局域网(LAN)、广域网(WAN)、区块链网络和互联网。
计算系统可以包括客户端和服务器。客户端和服务器一般远离彼此并且通常通过通信网络进行交互。通过在相应的计算机上运行并且彼此具有客户端-服务器关系的计算机程序来产生客户端和服务器的关系。服务器可以是云服务器，又称为云计算服务器或云主机，是云计算服务体系中的一项主机产品，以避免相关技术中物理主机与VPS服务中，存在的管理难度大，业务扩展性弱的情况。
应该理解，可以使用上面所示的多种形式的流程，重新排序、增加或删除步骤。例如，本申请中记载的多个步骤可以并行地执行也可以顺序地执行也可 以不同的次序执行，只要能够实现本申请的技术方案所期望的结果，本文在此不进行限制。

Claims (10)

  1. 一种脂蛋白亚型组分划分方法，包括：
    获取待划分脂蛋白试剂的试剂扫描图像；
    基于预先确定的目标分割点位对所述试剂扫描图像进行低密度脂蛋白亚型组分的划分，所述目标分割点位基于多个样本脂蛋白试剂的样本扫描图像处理得到。
  2. 根据权利要求1所述的方法，还包括：
    获取多个样本脂蛋白试剂的样本扫描图像，对所述样本扫描图像进行处理得到单个样本图像；
    根据所述单个样本图像的灰度值波形图像确定所述单个样本图像对应的目标分割点位。
  3. 根据权利要求2所述的方法，其中，所述根据所述单个样本图像的灰度值波形图像确定所述单个样本图像对应的目标分割点位，包括：
    根据所述单个样本图像的灰度值波形图像确定所述单个样本图像对应的单个分界点位；
    基于所述单个分界点位，确定所述目标分割点位。
  4. 根据权利要求3所述的方法，其中，所述根据所述单个样本图像的灰度值波形图像确定所述单个样本图像对应的单个分界点位，包括：
    对所述灰度值波形图像进行波形划分，将相邻波形的连接点位作为所述单个分界点位。
  5. 根据权利要求3所述的方法，其中，所述基于所述单个分界点位，确定所述目标分割点位，包括：
    对所述单个分界点位进行统计学验证，确定所述单个分界点位的置信区间；
    基于所述单个分界点位的置信区间确定所述单个分界点位的组分分割比例；
    根据所述组分分割比例确定所述单个分界点位对应的目标分割点位。
  6. 根据权利要求5所述的方法，其中，所述基于所述单个分界点位的置信区间确定所述单个分界点位的组分分割比例，包括：
    将所述置信区间的中间值作为所述单个分界点位的组分分割比例。
  7. 根据权利要求5所述的方法，其中，所述根据所述组分分割比例确定所述单个分界点位对应的目标分割点位，包括：
    确定所述单个样本图像中，以极低密度脂蛋白的波峰为起点，以高密度脂蛋白的波峰为终点的距离总长；
    基于所述组分分割比例和所述距离总长得到所述目标分割点位。
  8. 一种脂蛋白亚型组分划分装置，包括：
    试剂图像获取模块，设置为获取待划分脂蛋白试剂的试剂扫描图像；
    试剂图像划分模块，设置为基于预先确定的目标分割点位对所述试剂扫描图像进行低密度脂蛋白亚型组分的划分，所述目标分割点位基于多个样本脂蛋白试剂的样本扫描图像处理得到。
  9. 一种电子设备，包括：
    至少一个处理器；以及
    与所述至少一个处理器通信连接的存储器；其中，
    所述存储器存储有可被所述至少一个处理器执行的计算机程序，所述计算机程序被所述至少一个处理器执行，以使所述至少一个处理器能够执行权利要求1-7中任一项所述的脂蛋白亚型组分划分方法。
  10. 一种计算机可读存储介质，所述计算机可读存储介质存储有计算机指令，所述计算机指令用于使处理器执行时实现权利要求1-7中任一项所述的脂蛋白亚型组分划分方法。
PCT/CN2023/095007 2022-07-20 2023-05-18 一种脂蛋白亚型组分划分方法、装置、设备及存储介质 WO2024016814A1 (zh)

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