Method, device and medium for determining the positive / negative and titer of an antibody

Abstract

Embodiments of the present disclosure relate to a method, a computing device, and a medium for determining the positive / negative and titer of an antibody. The method includes: applying an indirect immunofluorescence image of an antibody based on cells to a positive / negative determination model to determine whether the antibody is positive; if it is determined that the antibody is positive, determining a nuclear karyotype mask for the cell nucleus and a cytoplasmic karyotype mask for the cell based on the indirect immunofluorescence image; determining the category of the indirect immunofluorescence image based on the nuclear karyotype mask and the cytoplasmic karyotype mask; and determining the titer based on the category and the gray value of a partial image of the indirect immunofluorescence image corresponding to the nuclear karyotype mask or the cytoplasmic karyotype mask. By this method, the positive / negative of the antibody and the titer of the antibody can be determined quickly and accurately, the accuracy of titer discrimination is improved, and the efficiency is improved.

Description

Technical Field

[0001] The present disclosure generally relates to bioinformatics processing, and more particularly, to methods, devices, and storage media for predicting the titer of antibodies. Background Art

[0002] Cell-based antibodies can recognize various nuclear components and are present in many autoimmune diseases. In these autoimmune diseases, antinuclear antibodies all show positive rates to varying degrees. Therefore, the positive degree of antinuclear antibodies can be used as a basis for diagnosing many diseases. For example, rheumatoid erythematosus SLE.

[0003] Common methods for detecting antinuclear antibodies include indirect immunofluorescence assay, ELISA, radioimmunoassay, etc. Among them, indirect immunofluorescence assay is the most commonly used method for current detection. This method mainly determines positive or negative by manually observing the fluorescence intensity under a microscope. However, there are many subjective factors in this manual interpretation process, and the interpretation process is time-consuming. Therefore, with the development of computer technology, many methods for processing indirect immunofluorescence images using a computer have emerged. However, there are still many problems to be solved in the process of processing indirect immunofluorescence images. Summary of the Invention

[0004] The present disclosure provides a method, a computing device, and a computer storage medium for determining the positive or negative and titer of an antibody.

[0005] According to a first aspect of the present disclosure, there is provided a method for determining the positive or negative and titer of an antibody. The method includes: applying an indirect immunofluorescence image of a cell-based antibody to a positive or negative interpretation model to determine whether the antibody is positive; if it is determined that the antibody is positive, determining different nuclear type masks corresponding to different cell nuclear types or cytoplasmic nuclear types based on the indirect immunofluorescence image; determining the category of the indirect immunofluorescence image based on the nuclear type mask and the cytoplasmic nuclear type mask; and determining the titer based on the category and the gray values of partial images of the indirect immunofluorescence image corresponding to the nuclear type mask or the cytoplasmic nuclear type mask.

[0006] According to a second aspect of the present invention, there is also provided a computing device, which includes: at least one processing unit; at least one memory, at least one memory being coupled to at least one processing unit and storing instructions for execution by at least one processing unit, the instructions when executed by at least one processing unit causing the computing device to execute the method of the first aspect of the present disclosure.

[0007] According to a third aspect of the present disclosure, there is also provided a computer-readable storage medium. Machine-executable instructions are stored on the computer-readable storage medium, and when the machine-executable instructions are executed, the machine executes the method of the first aspect of the present disclosure.

[0008] In some embodiments, applying the indirect immunofluorescence image to the positive / negative discrimination model includes: obtaining an indirect immunofluorescence image of an antibody with cells as the matrix; using a set of pixel values of pixels around a target pixel in the indirect immunofluorescence image to adjust the pixel value of the target pixel to generate an adjusted indirect immunofluorescence image; and applying the adjusted indirect immunofluorescence image to the positive / negative discrimination model to determine whether the antibody is positive.

[0009] In some embodiments, applying the adjusted indirect immunofluorescence image to the positive / negative discrimination model includes: adjusting the size of the adjusted indirect immunofluorescence image for application to the positive / negative discrimination model.

[0010] In some embodiments, determining the nuclear karyotype mask and the cytoplasmic karyotype mask includes: segmenting the indirect immunofluorescence image using a first gray value in a gray value range corresponding to the indirect immunofluorescence image to obtain a first mask, where the first mask indicates pixels in the indirect immunofluorescence image with gray values greater than the first gray value; segmenting the indirect immunofluorescence image using a second gray value in all gray values to obtain a second mask, where the second mask indicates pixels in the indirect immunofluorescence image with gray values greater than the second gray value; determining the nuclear karyotype mask and the cytoplasmic karyotype mask based on the first mask and the second mask.

[0011] In some embodiments, determining the nuclear karyotype mask and the cytoplasmic karyotype mask based on the first mask and the second mask includes: performing an AND operation on the first mask and the second mask to obtain the nuclear karyotype mask; performing an XOR operation on the first mask and the second mask to determine the cytoplasmic karyotype mask.

[0012] In some embodiments, determining the category includes: applying the nuclear karyotype mask and the cytoplasmic karyotype mask to a classification model to determine the category of the indirect immunofluorescence image, where the classification model is trained through a residual neural network.

[0013] In some embodiments, determining the titer includes: if it is determined that the category is nuclear type, determining a first gray value range of a plurality of pixels in the indirect immunofluorescence image corresponding to the nuclear karyotype mask; if it is determined that the category is cytoplasmic type, determining a second gray value range of a plurality of pixels in the indirect immunofluorescence image corresponding to the cytoplasmic karyotype mask; obtaining a mapping relationship between the gray value range and the titer, and based on the mapping relationship, determining the titer corresponding to the first gray value range or the second gray value range.

[0014] In some embodiments, the positive / negative discrimination model is trained using a residual neural network, and the training process utilizes a training data set, a test data set, and an evaluation data set.

[0015] The Summary of the Invention section is provided to introduce, in a simplified form, a selection of concepts that will be further described in the Detailed Description below. The Summary of the Invention section is not intended to identify key or essential features of the present disclosure, nor is it intended to limit the scope of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 FIG. 6 shows a schematic diagram of a system 100 for determining the positive / negative and titer of an antibody according to an embodiment of the present disclosure.

[0017] Figure 2 FIG. 10 shows a flowchart of a method 200 for determining the positive / negative and titer of an antibody according to an embodiment of the present disclosure.

[0018] Figure 3 FIG. 14 shows a schematic diagram of positive / negative cell images according to an embodiment of the present disclosure.

[0019] Figure 4 FIG. 18 shows a schematic diagram of a process 400 for training a positive / negative module according to an embodiment of the present disclosure.

[0020] Figure 5 FIG. 22 shows a schematic diagram of extracting a nuclear karyotype mask and a cytoplasmic karyotype mask according to an embodiment of the present disclosure.

[0021] Figure 6 FIG. 26 shows a schematic diagram for grayscale value extraction according to an embodiment of the present disclosure.

[0022] Figure 7 FIG. 30 shows a flowchart of a method 700 for preprocessing image data according to an embodiment of the present disclosure.

[0023] Figure 8 FIG. 34 shows a flowchart of a method 800 for determining a nuclear karyotype mask and a cytoplasmic karyotype mask according to an embodiment of the present disclosure.

[0024] Figure 9 FIG. 38 schematically shows a block diagram of an electronic device 900 suitable for implementing embodiments of the present disclosure.

[0025] In each of the drawings, the same or corresponding reference numerals denote the same or corresponding parts. DETAILED DESCRIPTION

[0026] Preferred embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the preferred embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided so that the present disclosure will be thorough and complete, and will fully convey the scope of the present disclosure to those skilled in the art.

[0027] As used herein, the term "comprising" and its variations mean open-ended inclusion, i.e., "including but not limited to". Unless otherwise specified, the term "or" means "and / or". The term "based on" means "at least partially based on". The terms "an example embodiment" and "an embodiment" mean "at least one example embodiment". The term "another embodiment" means "at least one additional embodiment". The terms "first", "second", etc. may refer to different or the same objects.

[0028] As described above, there are many subjective factors in the manual interpretation process, and the interpretation process is time-consuming. To solve these problems, some traditional solutions use computers for automatic interpretation. In these solutions, various set data processing models are adopted to process image data to identify the positive and negative and titer of antibodies based on cells. These traditional solutions have improved the determination accuracy of positive and negative and titer to a certain extent, but there are still certain precision bottlenecks and deficiencies. In addition to the precision aspect, some existing systems also have problems of low interpretation efficiency and certain limitations in generalization.

[0029] The main factors causing the precision bottleneck of the current related technology are as follows: Insufficient data. Real data often directly comes from clinical collection, so the collection is difficult. There are bottlenecks in the performance of the algorithm itself, and most use a single model for prediction. Therefore, it is difficult to improve the performance to the clinical requirements. For some cytoplasmic karyotypes, it is impossible to calculate the titer by separating the cytoplasmic and nuclear karyotypes. This leads to inaccurate titer calculation.

[0030] To at least partially solve one or more of the above problems and other potential problems, example embodiments of the present disclosure propose a solution for determining the positive and negative and titer of an antibody. In this solution, the computing device applies the indirect immunofluorescence image of the antibody based on cells to a positive and negative interpretation model to determine whether the antibody is positive. If it is determined that the antibody is positive, the computing device then determines a nuclear karyotype mask for the cell nucleus and a cytoplasmic karyotype mask for the cell based on the indirect immunofluorescence image. Next, the computing device determines the category of the indirect immunofluorescence image based on the nuclear karyotype mask and the cytoplasmic karyotype mask. Finally, the computing device determines the titer based on the category and the gray value of the nuclear karyotype mask or the cytoplasmic karyotype mask. Through this method, the positive and negative of the antibody and the titer of the antibody can be determined quickly and accurately, the precision of titer discrimination is improved, and the efficiency is enhanced.

[0031] Figure 1 FIG. shows a schematic diagram of a system 100 for determining the positive and negative and titer of an antibody according to an embodiment of the present disclosure. As Figure 1 shown, the system 100 includes, for example, a computing device 104.

[0032] The computing device 104 is configured to receive an indirect immunofluorescence image 102, and then process the indirect immunofluorescence image 102 to determine whether there is a positive antibody and, when the antibody is positive, determine the titer 110 for the antibody. The titer refers to the lowest concentration required to measure the ability of a certain antibody to recognize a specific antigen epitope. In the present disclosure, it mainly refers to evaluating the actual titer of the antibody based on the fluorescence intensity of the image.

[0033] Examples of the computing device 104 include, but are not limited to, personal computers, server computers, handheld or laptop devices, mobile devices (such as mobile phones, personal digital assistants (PDAs), media players, etc.), multi-processor systems, consumer electronics, minicomputers, mainframe computers, distributed computing environments including any one of the above systems or devices, etc.

[0034] The computing device 104 includes a positive / negative discrimination model 106 and a titer discrimination module 108. The positive / negative discrimination model 106 is used to determine whether the antibody in the image 102 is negative or positive. The positive / negative discrimination model 106 is a trained machine learning model. In some embodiments, the positive / negative discrimination model is obtained by training a residual neural network, such as Resnet 18. In some embodiments, the positive / negative discrimination model 106 is any suitable machine learning model that can classify images. The above examples are only for describing the present disclosure and do not specifically limit the present disclosure.

[0035] If, after being discriminated by the positive / negative discrimination model 106, the antibody in the image 102 is negative, then there is no need to further process the image 102. If the antibody in the image 102 is positive, then it needs to be further processed. At this time, the image 102 is input into the titer discrimination module 108 to determine the titer corresponding to the positive antibody.

[0036] Through this method, it is possible to quickly and accurately determine the positive / negative of the antibody and the titer of the antibody, improving the accuracy of titer discrimination and the efficiency.

[0037] Above Figure 1 shows a schematic diagram of a system 100 in which multiple embodiments of the present disclosure can be implemented. The following Figure 2 describes a flowchart of a method 200 for determining the positive / negative and titer of an antibody according to an embodiment of the present disclosure. The method 200 can be implemented by Figure 1 the computing device 104 or any other suitable device therein.

[0038] At block 202, the computing device 104 applies an indirect immunofluorescence image of an antibody with cells as the matrix to the positive / negative discrimination model to determine whether the antibody is positive. The computing device 104 uses the trained positive / negative discrimination model to determine the positivity of the antibody. As Figure 3As shown, it shows positive and negative cell images according to an embodiment of the present disclosure. Figure 3 The image on the left is antibody-positive, and the image on the right is antibody-negative.

[0039] In addition, in some embodiments, the received indirect immunofluorescence images need to be preprocessed, and this preprocessing process will be described below in conjunction with Figure 7 for description.

[0040] In some embodiments, the positive and negative interpretation model is trained using a residual neural network. In some embodiments, any suitable neural network or machine learning model can be used to generate it.

[0041] In some embodiments, during the process of training the positive and negative interpretation model, the training sample pictures are divided into a training data set, an evaluation data set, and a test data set to determine the trained positive and negative interpretation model. In some embodiments, the quantity ratio among the training data set, the evaluation data set, and the test data set is 7:2:1. In some embodiments, the quantity ratio among the training data set, the evaluation data set, and the test data set can be any other suitable value. As Figure 4 shown, the training data set 402, the evaluation data set 406, and the test data set 408 are used to train the positive and negative interpretation model 404. It mainly trains the positive and negative interpretation model 404 one or more times through the training data set 402, and then conducts an evaluation through the evaluation data set 406. Based on the evaluation result, it is determined whether further training is needed. If the evaluation result is not appropriate, it is trained again using the training data set 402. After the evaluation by the evaluation data set 406 is appropriate, the test data set 408 is used for testing. Through the test, the training of the positive and negative model is completed. Otherwise, continue training.

[0042] Return Figure 2 Then, for the three groups of data sets, predetermined processing can be performed to increase the data volume or improve the detection effect. For the preprocessing of the training data, refer to the following Figure 7 description.

[0043] In some embodiments, the images in the three groups of data sets are subjected to predetermined processing to increase the quantity of data in the data set, such as left-right flipping and up-down flipping. In some embodiments, the training sample pictures can be directly used as the training data set to train the positive and negative interpretation model.

[0044] In some embodiments, the size of the images in the data set is adjusted to adapt to the input of the positive and negative interpretation model. For example, the image data is downsampled or sampled in other suitable ways. The above examples are only used to describe the present disclosure and are not specific limitations of the present disclosure.

[0045] In some embodiments, the computing device 104 needs to adjust the size of the adjusted indirect immunofluorescence image for application to the positive / negative interpretation model. If the size of the indirect immunofluorescence image is smaller than the data input to the positive / negative interpretation model at one time, no size adjustment is required.

[0046] At block 204, if the computing device 104 determines that the antibody is positive, it determines a nuclear karyotype mask for the cell nucleus and a cytoplasmic karyotype mask for the cell based on the indirect immunofluorescence image. As Figure 5 shown, which shows the extracted nuclear karyotype mask and cytoplasmic karyotype mask according to an embodiment of the present disclosure, in Figure 5 the upper two images are the cell nucleus and the nuclear karyotype mask corresponding to the cell nucleus, Figure 5 and the lower two images in are the cytoplasm and the cytoplasmic karyotype mask corresponding to the cytoplasm. The process of obtaining the nuclear karyotype mask and cytoplasmic karyotype mask will be described below in Figure 8 .

[0047] At block 206, the computing device 104 determines the category of the indirect immunofluorescence image based on the nuclear karyotype mask and the cytoplasmic karyotype mask. The computing device determines the category of the image by inputting the mask data into a classification model.

[0048] In some embodiments, the computing device 104 applies the nuclear karyotype mask and the cytoplasmic karyotype mask to a classification model to determine the category of the indirect immunofluorescence image. In one example, the classification model is obtained by training a residual neural network. In another example, the classification model is obtained by training other machine learning models.

[0049] At block 208, the computing device 104 determines the titer based on the category and the gray values of the partial image of the indirect immunofluorescence image corresponding to the nuclear karyotype mask or the cytoplasmic karyotype mask.

[0050] In this process, the mapping relationship between the titer and the gray value is required. This mapping relationship is determined based on the statistical data of the titer at which resistance is observed. It can be divided into multiple levels within its numerical range, and each level has a corresponding gray value range. The above are only examples and not specific limitations of the present disclosure. Those skilled in the art can set the corresponding relationship between the titer and the gray value according to needs.

[0051] In some embodiments, if it is determined that the category is nuclear type, the computing device 104 determines the first gray value range of multiple pixels in the indirect immunofluorescence image corresponding to the nuclear karyotype mask. If it is determined that the category is cytoplasmic type, the computing device 104 determines the second gray value range of multiple pixels in the indirect immunofluorescence image corresponding to the cytoplasmic karyotype mask. As Figure 6As shown, it shows a schematic diagram for grayscale value acquisition according to an embodiment of the present disclosure. For example, for a pixel in the mask, the pixel value of the pixel corresponding to it in the indirect immunofluorescence image is determined. The computing device 104 also obtains the mapping relationship between the grayscale value range and the titer. Then, based on the mapping relationship, the titer corresponding to the first grayscale value range or the second grayscale value range is determined.

[0052] Through the above method, the positivity and titer of the antibody can be determined quickly and accurately, thereby improving the accuracy of titer determination and improving efficiency.

[0053] Combined with the above Figure 2-6 The procedures used to determine positivity and titer are described below. Figure 7 A flow chart depicting a method 700 for pre-processing image data.

[0054] At block 702, the computing device 104 acquires the cell-based antibody indirect immunofluorescence image 102. Next, at block 704, the computing device 104 adjusts the pixel value of the target pixel using a set of pixel values ​​of pixels surrounding the target pixel in the indirect immunofluorescence image to generate an adjusted indirect immunofluorescence image.

[0055] In some embodiments, for each pixel in the image, the size of the pixel values ​​of the pixels around the pixel is determined. For example, for each pixel, a sliding window including nine pixels is determined, and the window is three rows and three columns, with the pixel at the center. Then the pixel values ​​of the other eight pixels around are determined. Next, the pixel values ​​of the surrounding pixels are sorted to determine the minimum pixel value and the maximum pixel value. If the central pixel value is less than the minimum pixel value, the central pixel value is determined as the minimum pixel value. If the central pixel value is greater than the minimum value but less than the maximum value, the central pixel value is set to the maximum value. In some examples, a sliding window including other suitable numbers of pixels can be used. The above examples are only used to describe the present disclosure, and are not specifically limited to the present disclosure.

[0056] At block 706, the computing device 104 applies the adjusted indirect immunofluorescence image to a positive / negative judgment model to determine whether the antibody is positive. In this way, the brightness of pixels that are positive can be significantly increased.

[0057] In some embodiments, the above preprocessing is performed on the images in the three data sets used to train the positive / negative discrimination model to improve the training data sets.

[0058] above Figure 7 A flowchart of a method 700 for preprocessing image data is described below. Figure 8 A flow chart describing a method 800 for determining a nuclear karyon type mask and a cytoplasmic karyon type mask according to an embodiment of the present disclosure.

[0059] At block 802, the computing device 104 segments the indirect immunofluorescence image using a first grayscale value within the range of grayscale values corresponding to the indirect immunofluorescence image to obtain a first mask, where the first mask indicates the pixels in the indirect immunofluorescence image whose grayscale values are greater than the first grayscale value. In one example, the first grayscale value may be the intermediate grayscale value between the maximum grayscale value and the minimum grayscale value in the indirect immunofluorescence image. In another example, the first grayscale value may be an arbitrarily selected grayscale value between the maximum grayscale value and the minimum grayscale value in the indirect immunofluorescence image. The above examples are only for describing the present disclosure and are not specific limitations on the present disclosure. In the above manner, an initial nuclear karyotype mask can be determined.

[0060] At block 804, the computing device 104 segments the indirect immunofluorescence image using a second grayscale value among all the grayscale values to obtain a second mask, where the second mask indicates the pixels in the indirect immunofluorescence image whose grayscale values are greater than the second grayscale value. In one example, the second grayscale value is the intermediate grayscale value of all the grayscale values. In another example, the second grayscale value is a suitable grayscale value selected from all the grayscale values. The above examples are only for describing the present disclosure and are not specific limitations on the present disclosure. In the above manner, a mask including the nuclei and cytoplasm presenting positive can be determined.

[0061] At block 806, the computing device 104 uses the determined first mask and second mask to determine a nuclear karyotype mask and a cytoplasmic karyotype mask. In some embodiments, the computing device 104 performs an AND operation on the first mask and the second mask to obtain the nuclear karyotype mask. Next, the computing device performs an XOR operation on the first mask and the second mask to determine the cytoplasmic karyotype mask. In some embodiments, the computing device 104 performs an AND operation on the first mask and the second mask, and then performs a negation operation to obtain the nuclear karyotype mask and the cytoplasmic karyotype mask. The above examples are only for describing the present disclosure and are not specific limitations on the present disclosure.

[0062] Figure 9 A block diagram of an electronic device 900 suitable for implementing the embodiments of the present disclosure is schematically shown. The electronic device 900 can be used to implement the computing device 104 in claim 1. The device 900 can be used to implement Figure 2 Method 200, Figure 7 Method 700 and Figure 8 Method 800. As Figure 9As shown, device 900 includes a central processing unit (CPU) 901, which can perform various appropriate actions and processes according to computer program instructions stored in a read-only memory (ROM) 902 or computer program instructions loaded from a storage unit 908 into a random access memory (RAM) 903. In the RAM 903, various programs and data required for the operation of the device 900 can also be stored. The CPU 901, ROM 902, and RAM 903 are connected to each other via a bus 904. An input / output (I / O) interface 905 is also connected to the bus 904.

[0063] Multiple components in the device 900 are connected to the I / O interface 905, including: an input unit 906, an output unit 907, a storage unit 908. The processing unit 901 executes the various methods and processes described above, such as executing methods 200, 700, and 800. For example, in some embodiments, methods 200, 700, and 800 may be implemented as computer software programs, which are stored in a machine-readable medium, such as the storage unit 908. In some embodiments, part or all of the computer program may be loaded and / or installed onto the device 900 via the ROM 902 and / or a communication unit 909. When the computer program is loaded into the RAM 903 and executed by the CPU 901, one or more operations of the methods 200, 700, and 800 described above can be performed. Alternatively, in other embodiments, the CPU 901 may be configured to perform one or more actions of methods 200, 700, and 800 in any other suitable manner (e.g., by means of firmware).

[0064] It should be further noted that the present disclosure can be a method, apparatus, system, and / or computer program product. The computer program product may include a computer-readable storage medium having thereon computer-readable program instructions for performing various aspects of the present disclosure.

[0065] A computer-readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. A computer-readable storage medium may be, for example, but is not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disc (DVD), a memory stick, a floppy disk, a mechanically encoded device such as a punched card or raised structures in grooves having instructions stored thereon, and any suitable combination of the foregoing. The computer-readable storage medium as used herein is not construed to be a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., an optical pulse through an optical fiber cable), or an electrical signal transmitted through a wire.

[0066] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to respective computing / processing devices, or can be downloaded to an external computer or an external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network may include a copper transmission cable, an optical fiber transmission, a wireless transmission, a router, a firewall, a switch, a gateway computer, and / or an edge server. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in each computing / processing device.

[0067] The computer program instructions for performing the operations of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine - related instructions, microcode, firmware instructions, state - setting data, or source code or object code written in any combination of one or more programming languages, including object - oriented programming languages such as Smalltalk, C++, etc., and conventional procedural programming languages such as the "C" language or similar programming languages. The computer - readable program instructions may be executed entirely on the user's computer, partially on the user's computer, executed as a stand - alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via an Internet service provider through the Internet). In some embodiments, by using the state information of the computer - readable program instructions to customize an electronic circuit, such as a programmable logic circuit, a field - programmable gate array (FPGA), or a programmable logic array (PLA), the electronic circuit can execute the computer - readable program instructions to implement various aspects of the present disclosure.

[0068] Aspects of the present disclosure are described herein with reference to the flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present disclosure. It should be understood that each block of the flowcharts and / or block diagrams, and combinations of blocks in the flowcharts and / or block diagrams, can be implemented by computer - readable program instructions.

[0069] These computer - readable program instructions can be provided to the processing unit of a processor in a voice interaction device, a general - purpose computer, a special - purpose computer, or other programmable data - processing device, thereby producing a machine such that when these instructions are executed by the processing unit of the computer or other programmable data - processing device, a device is produced that implements the functions / actions specified in one or more blocks of the flowchart and / or block diagram. These computer - readable program instructions can also be stored in a computer - readable storage medium, which causes the computer, programmable data - processing device, and / or other devices to operate in a specific manner. Thus, the computer - readable medium storing the instructions includes a manufacture, which includes instructions for implementing various aspects of the functions / actions specified in one or more blocks of the flowchart and / or block diagram.

[0070] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device, causing a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process such that the instructions executed on the computer, other programmable data processing apparatus, or other device implement the functions / acts specified in one or more boxes of the flowchart and / or block diagram.

[0071] The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of devices, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagram may represent a module, a segment of code, or a portion of an instruction, which contains one or more executable instructions for implementing the specified logical function. In some alternative implementations, the functions noted in the blocks may occur out of the order noted in the figures. For example, two consecutive blocks may in fact be executed substantially in parallel, or they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented by a dedicated hardware-based system that performs the specified functions or acts, or by a combination of dedicated hardware and computer instructions.

[0072] The embodiments of the present disclosure have been described above. The above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art in the field without departing from the scope and spirit of the described embodiments. The choice of terms used herein is intended to best explain the principles of the embodiments, practical applications, or improvements to technologies in the market, or to enable other ordinary skill in the art in the field to understand the embodiments disclosed herein.

[0073] The above are only optional embodiments of the present disclosure and are not intended to limit the present disclosure. Various changes and modifications can be made to the present disclosure for those skilled in the art. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present disclosure shall be included within the protection scope of the present disclosure.

Claims

1. A method for determining the positive / negative status and titer of an antibody, comprising: applying an indirect immunofluorescence image of an antibody based on cells to a positive / negative interpretation model to determine whether the antibody is positive; if it is determined that the antibody is positive, determining a nuclear karyotype mask for the cell nucleus and a cytoplasmic karyotype mask for the cell based on the indirect immunofluorescence image; determining the category of the indirect immunofluorescence image based on the nuclear karyotype mask and the cytoplasmic karyotype mask; and determining the titer based on the category and the gray values of a partial image of the indirect immunofluorescence image corresponding to the nuclear karyotype mask or the cytoplasmic karyotype mask; wherein determining the nuclear karyotype mask and the cytoplasmic karyotype mask includes: segmenting the indirect immunofluorescence image using a first gray value in a gray value range corresponding to the indirect immunofluorescence image to obtain a first mask, the first mask indicating pixels in the indirect immunofluorescence image with gray values greater than the first gray value; segmenting the indirect immunofluorescence image using a second gray value in all gray values to obtain a second mask, the second mask indicating pixels in the indirect immunofluorescence image with gray values greater than the second gray value; performing an AND operation on the first mask and the second mask to determine the nuclear karyotype mask; and performing an XOR operation on the first mask and the second mask to determine the cytoplasmic karyotype mask.

2. The method according to claim 1, wherein applying the indirect immunofluorescence image to the positive / negative interpretation model comprises: acquiring an indirect immunofluorescence image of an antibody based on cells; adjusting the pixel value of a target pixel using a set of pixel values of pixels around the target pixel in the indirect immunofluorescence image to generate an adjusted indirect immunofluorescence image; and applying the adjusted indirect immunofluorescence image to the positive / negative interpretation model to determine whether the antibody is positive.

3. The method according to claim 2, wherein applying the adjusted indirect immunofluorescence image to the positive / negative interpretation model comprises: adjusting the size of the adjusted indirect immunofluorescence image for application to the positive / negative interpretation model.

4. The method according to claim 1, wherein determining the category comprises: applying the nuclear karyotype mask and the cytoplasmic karyotype mask to a classification model to determine the category of the indirect immunofluorescence image, the classification model being obtained by training a residual neural network.

5. The method according to claim 1, wherein determining the titer comprises: if it is determined that the category is nuclear type, determining a first gray value range of multiple pixels in the indirect immunofluorescence image corresponding to the nuclear karyotype mask; if it is determined that the category is cytoplasmic type, determining a second gray value range of multiple pixels in the indirect immunofluorescence image corresponding to the cytoplasmic karyotype mask; acquiring the mapping relationship between the gray value range and the titer; and determining the titer corresponding to the first gray value range or the second gray value range based on the mapping relationship.

6. The method according to claim 1, wherein the positive and negative interpretation model is trained using a residual neural network, and the training process utilizes a training data set, a test data set, and an evaluation data set.

7. A computing device, comprising: at least one processing unit; at least one memory, the at least one memory being coupled to the at least one processing unit and storing instructions for execution by the at least one processing unit, the instructions when executed by the at least one processing unit causing the computing device to perform the method according to any one of claims 1 to 6.

8. A computer-readable storage medium having stored thereon machine-executable instructions that, when executed, cause a machine to perform the method according to any one of claims 1 to 6.

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