Method, device, equipment and medium for acquiring annotation points of biological sample images

Abstract

This application relates to the technical field of annotation point acquisition, and discloses a method, device, equipment, and medium for acquiring annotation points for biological sample images. The method includes: acquiring a first two-dimensional image and a second two-dimensional image; performing ridge line extraction based on the first two-dimensional image to obtain a first ridge line on the first two-dimensional image, and performing ridge line extraction based on the second two-dimensional image to obtain a second ridge line on the second two-dimensional image; performing superposition processing based on the first two-dimensional image and the second two-dimensional image to determine each maximum point pair; and determining target annotation points based on the first ridge line, the second ridge line, and the maximum point pair. The method can quickly use the first two-dimensional image and the second two-dimensional image to screen out target annotation points, thereby achieving rapid and low-cost acquisition of high-accuracy, high-precision, biologically meaningful annotation data that can be used for supervised learning.

Description

Technical Field

[0001] The present invention relates to the technical field of acquiring annotation points of biological sample images, and in particular to a method, device, equipment and medium for acquiring annotation points of biological sample images. Background Art

[0002] High-resolution imaging of large biological samples is an important requirement for neuroscience and biological research. However, when imaging larger samples (such as mouse brain, macaque brain, etc.), due to the limitations of current imaging technology and equipment, it is often necessary to cut the sample into several slices and image them layer by layer. A commonly used imaging strategy is to pre-cut the sample into several slices of equal thickness and scan them synchronously. At this time, there must be no overlap between the different slices of the imaged samples, which poses a new challenge for subsequent three-dimensional reconstruction: high-precision stitching of adjacent, non-overlapping, non-rigidly deformed three-dimensional microscopic images, that is, how to stitch the three-dimensional images of each layer without overlapping information into a complete three-dimensional image.

[0003] With the development of deep learning, people have come to appreciate its capabilities and advantages in image processing. Several methods using deep learning for registration and stitching have begun to emerge. However, because the acquisition of labeled data (supervisory information) is often expensive in the biological and medical fields, currently well-developed models are primarily unsupervised learning models, such as VoxelMorph. The main principle of unsupervised registration is to minimize the difference between the image to be registered and the standard image, or to maximize similarity. However, similar to global optimization methods, the similarity metric used here cannot be directly applied to adjacent surfaces of two non-overlapping three-dimensional images with subtle spatial differences. Therefore, the registration effect cannot meet high-precision requirements.

[0004] Supervised learning, on the other hand, designs the registration of annotated data into a loss function. This not only takes into account the issue of three-dimensional continuity, but also more directly and intuitively corresponds to the ultimate goal of registration: high-precision stitching of adjacent, non-overlapping, non-rigidly deformed three-dimensional microscopic images. A specific form of annotated data is defined here: pairs of matching points (with physical and biological significance) on adjacent two-dimensional surfaces of two layers of non-overlapping three-dimensional images, such as two corresponding breakpoints on adjacent two-dimensional cross-sections of a continuous structure (such as three-dimensional nerve fibers and blood vessels). However, as mentioned earlier, due to the high precision, complexity, and specificity of biological and medical images (each pixel in biological and medical images may have strict physical and biological significance, and therefore the tolerance for error is lower than that of natural and artificially generated images), the current acquisition of annotation points for biological sample images is relatively poor. Summary of the Invention

[0005] Based on this, it is necessary to address the technical problem that the existing technology of obtaining annotation points of biological sample images has poor effect, and propose a method, device, equipment and medium for obtaining annotation points of biological sample images.

[0006] In a first aspect, a method for acquiring annotation points of a biological sample image is provided, the method comprising:

[0007] Acquire a first two-dimensional image and a second two-dimensional image, wherein the first two-dimensional image and the second two-dimensional image are obtained by segmenting and projecting the three-dimensional biological sample image;

[0008] Performing ridge line extraction based on the first two-dimensional image to obtain first ridge lines on the first two-dimensional image, and performing ridge line extraction based on the second two-dimensional image to obtain second ridge lines on the second two-dimensional image, wherein each first ridge line corresponds to each second ridge line in a one-to-one manner;

[0009] Determining each maximum point pair based on a superposition process of the first two-dimensional image and the second two-dimensional image, wherein the maximum point pair includes a first maximum point on the first two-dimensional image and a second maximum point corresponding to the first maximum point on the second two-dimensional image;

[0010] A target annotation point is determined based on the first ridge line, the second ridge line, and the maximum point pair.

[0011] In a second aspect, a device for acquiring annotation points of a biological sample image is provided, the device comprising:

[0012] an acquisition module, configured to acquire a first two-dimensional image and a second two-dimensional image, wherein the first two-dimensional image and the second two-dimensional image are obtained by segmenting and projecting the three-dimensional biological sample image;

[0013] a ridge line extraction module, configured to perform ridge line extraction based on the first two-dimensional image to obtain first ridge lines on the first two-dimensional image, and to perform ridge line extraction based on the second two-dimensional image to obtain second ridge lines on the second two-dimensional image, wherein each first ridge line corresponds to each second ridge line in a one-to-one manner;

[0014] a superposition processing module, configured to perform superposition processing based on the first two-dimensional image and the second two-dimensional image to determine each maximum point pair, wherein the maximum point pair includes a first maximum point on the first two-dimensional image and a second maximum point corresponding to the first maximum point on the second two-dimensional image;

[0015] A determination module is used to determine a target annotation point based on the first ridge line, the second ridge line, and the maximum point pair.

[0016] In a third aspect, a computer device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned method for acquiring annotation points of a biological sample image when executing the computer program.

[0017] In a fourth aspect, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned method for acquiring annotation points of a biological sample image are implemented.

[0018] The present invention proposes a method for acquiring annotation points for a biological sample image, which obtains a first two-dimensional image and a second two-dimensional image, wherein the first two-dimensional image and the second two-dimensional image are obtained by segmenting and projecting a three-dimensional biological sample image, then performs ridge line extraction based on the first two-dimensional image to obtain a first ridge line on the first two-dimensional image, and performs ridge line extraction based on the second two-dimensional image to obtain a second ridge line on the second two-dimensional image, wherein each first ridge line corresponds to each second ridge line one-to-one, then performs superposition processing based on the first two-dimensional image and the second two-dimensional image to determine each maximum point pair, wherein the maximum point pair includes a first maximum point on the first two-dimensional image and a second maximum point corresponding to the first maximum point on the second two-dimensional image, and finally determines a target annotation point based on the first ridge line, the second ridge line, and the maximum point pair. The method can quickly screen out the target annotation point using the first two-dimensional image and the second two-dimensional image, thereby achieving rapid and low-cost acquisition of high-accuracy, high-precision, biologically meaningful annotation data that can be used for supervised learning. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. 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 paying any creative work.

[0020] in:

[0021] Figure 1 A diagram illustrating an application environment of a method for acquiring annotation points of a biological sample image in one embodiment;

[0022] Figure 2 is a flow chart of a method for acquiring annotation points of a biological sample image in one embodiment;

[0023] Figure 3is a structural block diagram of a device for acquiring annotation points of a biological sample image in one embodiment;

[0024] Figure 4 is a structural block diagram of a computer device in one embodiment;

[0025] Figure 5 It is a structural block diagram of a computer device in another embodiment. DETAILED DESCRIPTION

[0026] Unless otherwise defined, all technical and scientific terms used herein have the same meanings as commonly understood by those skilled in the art to which this application belongs. The terms used in the specification of the application are for the purpose of describing specific embodiments only and are not intended to limit this application. The terms "including" and "having" and any variations thereof in the specification and claims of this application and the above-mentioned drawings are intended to cover non-exclusive inclusions. The terms "first", "second", etc. in the specification and claims of this application or the above-mentioned drawings are used to distinguish different objects, not to describe a specific order.

[0027] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0028] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0029] The method for obtaining annotation points of biological sample images provided by the embodiment of the present invention can be applied in the following situations: Figure 1In an application environment, wherein the client 110 communicates with the server 120 via a network. The server 120 can receive a first two-dimensional image and a second two-dimensional image through the client 110, wherein the first two-dimensional image and the second two-dimensional image are obtained by segmentation and projection processing based on a three-dimensional biological sample image. Then, ridge line extraction is performed based on the first two-dimensional image to obtain a first ridge line on the first two-dimensional image, and ridge line extraction is performed based on the second two-dimensional image to obtain a second ridge line on the second two-dimensional image, wherein each first ridge line corresponds to each second ridge line. Then, a superposition process is performed based on the first two-dimensional image and the second two-dimensional image to determine each maximum point pair, wherein the maximum point pair includes a first maximum point on the first two-dimensional image and a second maximum point corresponding to the first maximum point on the second two-dimensional image. Finally, target annotation points are determined based on the first ridge line, the second ridge line, and the maximum point pair. The target annotation points can be quickly screened out using the first two-dimensional image and the second two-dimensional image, thereby achieving rapid and low-cost acquisition of highly accurate, high-precision, and biologically meaningful annotation data that can be used for supervised learning. The client 110 can be, but is not limited to, various personal computers, laptops, smartphones, tablets, and portable wearable devices. The server 120 can be implemented as an independent server or a server cluster composed of multiple servers. The present invention will be described in detail below through specific embodiments.

[0030] See also Figure 2 As shown, Figure 2 A flowchart of a method for acquiring annotation points of a biological sample image provided by an embodiment of the present invention includes the following steps:

[0031] Step S101: Acquire a first two-dimensional image and a second two-dimensional image, wherein the first two-dimensional image and the second two-dimensional image are obtained by segmenting and projecting a three-dimensional biological sample image;

[0032] The three-dimensional biological sample image may refer to adjacent, non-overlapping, and non-rigidly deformed three-dimensional microscopic images.

[0033] As an example, the original high-resolution three-dimensional biological sample image is first cropped into image blocks of uniform size, such as 256x256x256 pixels. This step is intended to ensure the consistency of subsequent processing and reduce the processing burden of a single data block. When cropping, it is necessary to ensure the integrity of the region of interest and that the cropped block contains sufficient information for subsequent analysis. Each cropped image block is evenly divided into two parts, upper and lower, according to the Z-axis coordinate, and then the two parts are projected along the Z-axis to obtain two two-dimensional images, one is the first two-dimensional image and the other is the second two-dimensional image. The purpose of this step is to simplify the three-dimensional data into two-dimensional data while retaining key information to support subsequent image registration and stitching. The first two-dimensional image and the second two-dimensional image are named fixed image and moving image, respectively.

[0034] Step S102: performing ridge line extraction based on the first two-dimensional image to obtain first ridge lines on the first two-dimensional image, and performing ridge line extraction based on the second two-dimensional image to obtain second ridge lines on the second two-dimensional image, wherein each first ridge line corresponds to each second ridge line in a one-to-one manner;

[0035] In a preferred implementation, the first two-dimensional image and the second two-dimensional image are preprocessed separately, wherein the preprocessing includes contrast enhancement, depth conversion, binarization, and preliminary noise filtering; ridge extraction is performed based on the preprocessed first two-dimensional image to obtain a first ridge on the first two-dimensional image; and ridge extraction is performed based on the preprocessed second two-dimensional image to obtain a second ridge on the second two-dimensional image. For example, an image processing tool is used to automatically enhance the contrast of fixed and moving images. The main purpose of this step is to improve the recognizability of features in the image and improve the overall quality of the image. Contrast enhancement helps to highlight the details and structure of the image, making subsequent feature extraction and image analysis more effective. Depth conversion can convert an image from 16 bits to 8 bits. This step can significantly reduce the processing complexity and storage requirements of the data. At the same time, for most image processing and analysis tasks, 8-bit images already provide sufficient information. The fixed and moving images are binarized and converted into black and white images. During the binarization process, each pixel in the image is classified as either brightest or darkest, which helps to more clearly distinguish between the objects in the image (objects are three-dimensional continuous structures such as neurons and blood vessels) and the background. The final step is to perform noise filtering on the binarized fixed and moving images. By calculating all connected components in the image and filtering out connected components with too small an area, image noise is reduced. This step helps remove unnecessary small objects and stray pixels in the image, reducing interference with subsequent image processing and analysis. Initial noise filtering is a key step to ensure image quality and the accuracy of subsequent processing.

[0036] As an example, ridge detection technology is used to extract ridge features in the first two-dimensional image (fixed image) and the second two-dimensional image (moving image), which are respectively denoted as l f and l m , and we also get l f The corresponding line width is w f and l m The corresponding line width w m and other relevant properties. Ridge detection helps identify major structures in an image, such as nerve fibers or blood vessels, which are crucial in 3D microscopic image stitching.

[0037] Step S103: performing superposition processing on the first two-dimensional image and the second two-dimensional image to determine each maximum point pair, wherein the maximum point pair includes a first maximum point on the first two-dimensional image and a second maximum point corresponding to the first maximum point on the second two-dimensional image;

[0038] In a preferred implementation, the first two-dimensional image (fixed image) and the second two-dimensional image (moving image) are superimposed by channel to obtain a superimposed image. The superimposed image is subjected to a maximum point detection (find-maximum) to identify points whose brightness value exceeds a specific threshold (for example, the specific threshold is 254), which is recorded as p m (x0,y0), (x0,y0) is p m The coordinates on the superimposed image by which p can be determined on the first two-dimensional image m The maximum point corresponding to (x0, y0) is taken as the first maximum point, and the coordinates can be used to determine p on the second two-dimensional image. m The maximum point corresponding to (x0, y0) is taken as the second maximum point, and the maximum point pair refers to the first maximum point and the second maximum point. The above steps are intended to identify potential labeled point pairs in the superimposed image. It should be noted that the intersection points of all three-dimensional continuous structures on the two-dimensional projection map correspond to potential labeled point pairs. The physical meaning of the labeled point pairs: mainly refers to three-dimensional continuous nerve fibers. When the three-dimensional image block is cut in the middle, the breakpoints left by the nerve fibers on the two adjacent two-dimensional surfaces in the middle of the upper and lower images may also be similar structures such as blood vessels in other images.

[0039] Step S104: determining a target annotation point based on the first ridge line, the second ridge line, and the maximum point pair.

[0040] In a preferred implementation, step S104 includes:

[0041] Step S1041: determining the first ridge line and the second ridge line corresponding to the maximum point pair as a ridge line combination;

[0042] It should be noted that the first ridge line and the second ridge line corresponding to the maximum point pair are obtained by cutting a three-dimensional continuous nerve fiber or other continuous tissue. The ridge line combination corresponding to each maximum point pair can be determined by traversing or manually setting a threshold.

[0043] Step S1042: For each maximum point pair, calculate the shortest distance between the first maximum point in the maximum point pair and the first ridge line in the ridge line combination to obtain a first distance, and calculate the shortest distance between the second maximum point in the maximum point pair and the second ridge line in the ridge line combination to obtain a second distance;

[0044] Step S1043: screening the maximum value point pairs based on the first distance, the second distance, the line width of the first ridge line in the ridge line combination, and the line width of the second ridge line in the ridge line combination;

[0045] Step S1044: Determine the target annotation point based on the filtered maximum point pair and the ridge line combination.

[0046] As an example, the first distance and the second distance are respectively denoted as d f and d m , retain the first distance d f Less than the first line width threshold, the second distance d m The maximum point pair whose absolute value of the difference between the line width of the first ridge line and the line width of the second ridge line is less than the second line width threshold and is less than the preset value is completed, wherein the first line width threshold is half of the line width w of the first ridge line. f , the second line width threshold is half the line width w of the second ridge line m The preset value is set by previous experience. It should be noted that if the absolute value of the difference between the line width of the first ridge line and the line width of the second ridge line is less than the preset value, it means that the two ridge lines are more likely to be the same nerve fiber. Otherwise, it may be an interference signal, and this maximum point pair is filtered out.

[0047] In one embodiment, the step of determining the target annotation point based on the filtered maximum point pair and the ridge line combination includes:

[0048] Step S10411: For each of the filtered maximum point pairs, determine, on the first ridge line of the ridge line combination corresponding to the filtered maximum point pair, a point closest to the first maximum point in the filtered maximum point pair as a first target point;

[0049] Step S10412: Determine a tangent vector of the first target point on the first ridge line in the ridge line combination corresponding to the filtered maximum point pair as a first tangent vector;

[0050] Step S10413: For each of the filtered maximum point pairs, determine, on the second ridge line of the ridge line combination corresponding to the filtered maximum point pair, a point closest to the second maximum point in the filtered maximum point pair as the second target point;

[0051] Step S10414: determining a tangent vector of the second target point on the second ridge line in the ridge line combination corresponding to the filtered maximum point as a second tangent vector;

[0052] Step S10415: Based on the distance between the first target point and the second target point, the first tangent vector, and the second tangent vector, determining whether the first target point and the second target point meet a preset marking condition;

[0053] As an example, determine whether the distance between the first target point and the second target point is less than a set value. If so, determine whether the angle θ between the first tangent vector and the second tangent vector is less than a threshold β. If so, the first target point and the second target point meet the preset marking conditions.

[0054] Step S10416: If the first target point and the second target point meet the preset marking condition, the first target point and the second target point are used as target marking points.

[0055] As an example, after obtaining the target annotation point, data augmentation operations are performed on the second two-dimensional image (moving image) and its second target point. This step is not aimed at directly improving the image quality, but rather increasing the diversity of the data and the robustness of the model by simulating possible changes. Data augmentation operations include but are not limited to: Random displacement: performing slight random displacements on the image to simulate the position changes that may occur in the sample during the actual imaging process. Random deformation: applying nonlinear transformations to simulate deformations that the sample may encounter during the imaging process, such as compression or stretching. This helps the training model adapt to various deformation conditions. Other transformations: may also include other types of transformations such as random rotation and scaling to further enrich the training data set.

[0056] As another example, during the training process, the second two-dimensional image after the model prediction adjustment is returned as the second two-dimensional image to execute step 101. This means that at different stages of model training, the system will continuously analyze the deformed moving image to detect and annotate new feature point pairs. This step ensures that as the model learning progresses, new and potentially more accurate annotated point pairs can be identified and utilized. For newly detected and qualified annotated point pairs during the training process, the system will add these point pairs to the annotation information of the fixed and moving images in the original training set.

[0057] The method for acquiring annotation points for a biological sample image proposed in this embodiment obtains a first two-dimensional image and a second two-dimensional image, wherein the first two-dimensional image and the second two-dimensional image are obtained by segmenting and projecting a three-dimensional biological sample image. Then, ridge line extraction is performed based on the first two-dimensional image to obtain first ridge lines on the first two-dimensional image, and ridge line extraction is performed based on the second two-dimensional image to obtain second ridge lines on the second two-dimensional image, wherein each first ridge line corresponds to each second ridge line one-to-one. Then, superposition processing is performed based on the first two-dimensional image and the second two-dimensional image to determine each maximum point pair, wherein the maximum point pair includes a first maximum point on the first two-dimensional image and a second maximum point corresponding to the first maximum point on the second two-dimensional image. Finally, target annotation points are determined based on the first ridge lines, the second ridge lines, and the maximum point pairs. In this way, the target annotation points can be quickly screened out using the first two-dimensional image and the second two-dimensional image, thereby achieving rapid and low-cost acquisition of highly accurate, high-precision, and biologically meaningful annotation data that can be used for supervised learning.

[0058] See also Figure 3 As shown, in one embodiment, a device for acquiring annotation points of a biological sample image is provided, the device comprising:

[0059] An acquisition module 10 is configured to acquire a first two-dimensional image and a second two-dimensional image, wherein the first two-dimensional image and the second two-dimensional image are obtained by segmenting and projecting the three-dimensional biological sample image;

[0060] a ridge line extraction module 20 configured to perform ridge line extraction based on the first two-dimensional image to obtain first ridge lines on the first two-dimensional image, and to perform ridge line extraction based on the second two-dimensional image to obtain second ridge lines on the second two-dimensional image, wherein each first ridge line corresponds to each second ridge line in a one-to-one manner;

[0061] an overlay processing module 30, configured to perform overlay processing based on the first two-dimensional image and the second two-dimensional image to determine each maximum point pair, wherein the maximum point pair includes a first maximum point on the first two-dimensional image and a second maximum point corresponding to the first maximum point on the second two-dimensional image;

[0062] The determination module 40 is configured to determine a target annotation point based on the first ridge line, the second ridge line, and the maximum point pair.

[0063] In one embodiment, the determination module 40 is used to: determine the first ridge line and the second ridge line corresponding to the maximum point pair as a ridge line combination; for each of the maximum point pairs, calculate the shortest distance between the first maximum point in the maximum point pair and the first ridge line in the ridge line combination to obtain a first distance, and calculate the shortest distance between the second maximum point in the maximum point pair and the second ridge line in the ridge line combination to obtain a second distance; based on the first distance, the second distance, the line width of the first ridge line in the ridge line combination, and the line width of the second ridge line in the ridge line combination, filter the maximum point pairs; and determine the target annotation point based on the filtered maximum point pairs and the ridge line combination.

[0064] In one embodiment, the determination module 40 is used to: for each of the filtered maximum point pairs, determine the point closest to the first maximum point in the filtered maximum point pair on the first ridge line in the ridge line combination corresponding to the filtered maximum point pair as the first target point; determine the tangent vector of the first target point on the first ridge line in the ridge line combination corresponding to the filtered maximum point pair as the first tangent vector; for each of the filtered maximum point pairs, determine the point closest to the second maximum point in the filtered maximum point pair on the second ridge line in the ridge line combination corresponding to the filtered maximum point pair as the second target point; determine the tangent vector of the second target point on the second ridge line in the ridge line combination corresponding to the filtered maximum point as the second tangent vector; based on the distance between the first target point and the second target point, the first tangent vector and the second tangent vector, determine whether the first target point and the second target point meet the preset labeling conditions; if the first target point and the second target point meet the preset labeling conditions, then the first target point and the second target point are used as target labeling points.

[0065] In one embodiment, the ridge line extraction module 20 is used to preprocess the first two-dimensional image and the second two-dimensional image respectively, wherein the preprocessing includes contrast enhancement, depth conversion, binarization processing, and preliminary noise filtering; ridge line extraction is performed based on the preprocessed first two-dimensional image to obtain a first ridge line on the first two-dimensional image; and ridge line extraction is performed based on the preprocessed second two-dimensional image to obtain a second ridge line on the second two-dimensional image.

[0066] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 4As shown. The computer device includes a processor, a memory, a network interface and a database connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile and / or volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external client via a network connection. When the computer program is executed by the processor, it implements the functions or steps on the server side of a method for obtaining annotation points of a biological sample image.

[0067] In one embodiment, a computer device is provided. The computer device may be a client, and its internal structure diagram may be as follows: Figure 5 As shown. The computer device includes a processor, memory, network interface, display screen, and input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external server via a network connection. When the computer program is executed by the processor, it implements the functions or steps on the client side of a method for obtaining annotation points of a biological sample image.

[0068] In one embodiment, a computer device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the following steps are implemented:

[0069] Acquire a first two-dimensional image and a second two-dimensional image, wherein the first two-dimensional image and the second two-dimensional image are obtained by segmenting and projecting the three-dimensional biological sample image;

[0070] Performing ridge line extraction based on the first two-dimensional image to obtain first ridge lines on the first two-dimensional image, and performing ridge line extraction based on the second two-dimensional image to obtain second ridge lines on the second two-dimensional image, wherein each first ridge line corresponds to each second ridge line in a one-to-one manner;

[0071] Determining each maximum point pair based on a superposition process of the first two-dimensional image and the second two-dimensional image, wherein the maximum point pair includes a first maximum point on the first two-dimensional image and a second maximum point corresponding to the first maximum point on the second two-dimensional image;

[0072] A target annotation point is determined based on the first ridge line, the second ridge line, and the maximum point pair.

[0073] The method for acquiring annotation points for a biological sample image proposed in this embodiment obtains a first two-dimensional image and a second two-dimensional image, wherein the first two-dimensional image and the second two-dimensional image are obtained by segmenting and projecting a three-dimensional biological sample image. Then, ridge line extraction is performed based on the first two-dimensional image to obtain first ridge lines on the first two-dimensional image, and ridge line extraction is performed based on the second two-dimensional image to obtain second ridge lines on the second two-dimensional image, wherein each first ridge line corresponds to each second ridge line one-to-one. Then, superposition processing is performed based on the first two-dimensional image and the second two-dimensional image to determine each maximum point pair, wherein the maximum point pair includes a first maximum point on the first two-dimensional image and a second maximum point corresponding to the first maximum point on the second two-dimensional image. Finally, target annotation points are determined based on the first ridge lines, the second ridge lines, and the maximum point pairs. In this way, the target annotation points can be quickly screened out using the first two-dimensional image and the second two-dimensional image, thereby achieving rapid and low-cost acquisition of highly accurate, high-precision, and biologically meaningful annotation data that can be used for supervised learning.

[0074] In one embodiment, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0075] Acquire a first two-dimensional image and a second two-dimensional image, wherein the first two-dimensional image and the second two-dimensional image are obtained by segmenting and projecting the three-dimensional biological sample image;

[0076] Performing ridge line extraction based on the first two-dimensional image to obtain first ridge lines on the first two-dimensional image, and performing ridge line extraction based on the second two-dimensional image to obtain second ridge lines on the second two-dimensional image, wherein each first ridge line corresponds to each second ridge line in a one-to-one manner;

[0077] Determining each maximum point pair based on a superposition process of the first two-dimensional image and the second two-dimensional image, wherein the maximum point pair includes a first maximum point on the first two-dimensional image and a second maximum point corresponding to the first maximum point on the second two-dimensional image;

[0078] A target annotation point is determined based on the first ridge line, the second ridge line, and the maximum point pair.

[0079] The method for acquiring annotation points for a biological sample image proposed in this embodiment obtains a first two-dimensional image and a second two-dimensional image, wherein the first two-dimensional image and the second two-dimensional image are obtained by segmenting and projecting a three-dimensional biological sample image. Then, ridge line extraction is performed based on the first two-dimensional image to obtain first ridge lines on the first two-dimensional image, and ridge line extraction is performed based on the second two-dimensional image to obtain second ridge lines on the second two-dimensional image, wherein each first ridge line corresponds to each second ridge line one-to-one. Then, superposition processing is performed based on the first two-dimensional image and the second two-dimensional image to determine each maximum point pair, wherein the maximum point pair includes a first maximum point on the first two-dimensional image and a second maximum point corresponding to the first maximum point on the second two-dimensional image. Finally, target annotation points are determined based on the first ridge lines, the second ridge lines, and the maximum point pairs. In this way, the target annotation points can be quickly screened out using the first two-dimensional image and the second two-dimensional image, thereby achieving rapid and low-cost acquisition of highly accurate, high-precision, and biologically meaningful annotation data that can be used for supervised learning.

[0080] It should be noted that the above functions or steps that can be implemented by the computer-readable storage medium or computer device can be found in the relevant descriptions of the server side and the client side in the aforementioned method embodiment. To avoid repetition, they will not be described one by one here.

[0081] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0082] Those skilled in the art will clearly understand that for the sake of convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0083] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention, and should all be included in the scope of protection of the present invention.

Claims

1. A method for acquiring annotation points of a biological sample image, characterized in that: The method for acquiring annotation points of a biological sample image includes: Acquire a first two-dimensional image and a second two-dimensional image, wherein the first two-dimensional image and the second two-dimensional image are obtained by segmenting and projecting the three-dimensional biological sample image; Performing ridge line extraction based on the first two-dimensional image to obtain first ridge lines on the first two-dimensional image, and performing ridge line extraction based on the second two-dimensional image to obtain second ridge lines on the second two-dimensional image, wherein each first ridge line corresponds to each second ridge line in a one-to-one manner; Determining each maximum point pair based on a superposition process of the first two-dimensional image and the second two-dimensional image, wherein the maximum point pair includes a first maximum point on the first two-dimensional image and a second maximum point corresponding to the first maximum point on the second two-dimensional image; Determine the first ridge line and the second ridge line corresponding to the maximum point pair as a ridge line combination; For each of the maximum point pairs, calculating the shortest distance between the first maximum point in the maximum point pair and the first ridge line in the ridge line combination to obtain a first distance, and calculating the shortest distance between the second maximum point in the maximum point pair and the second ridge line in the ridge line combination to obtain a second distance; screening the maximum value point pairs based on the first distance, the second distance, the line width of the first ridge line in the ridge line combination, and the line width of the second ridge line in the ridge line combination; For each of the filtered maximum point pairs, determining, on the first ridge line of the ridge line combination corresponding to the filtered maximum point pair, a point closest to the first maximum point in the filtered maximum point pair as a first target point; Determine a tangent vector of the first target point on the first ridge line in the ridge line combination corresponding to the filtered maximum point pair as a first tangent vector; For each of the filtered maximum point pairs, determining, on the second ridge line of the ridge line combination corresponding to the filtered maximum point pair, a point closest to the second maximum point in the filtered maximum point pair as the second target point; Determine a tangent vector of the second target point on the second ridge line in the ridge line combination corresponding to the filtered maximum point as a second tangent vector; Determining whether the first target point and the second target point meet a preset marking condition based on the distance between the first target point and the second target point, the first tangent vector, and the second tangent vector; If the first target point and the second target point meet the preset marking conditions, the first target point and the second target point are used as target marking points.

2. The method for acquiring annotation points of a biological sample image according to claim 1, wherein: The steps of extracting ridges based on the first two-dimensional image to obtain a first ridge on the first two-dimensional image, and extracting ridges based on the second two-dimensional image to obtain a second ridge on the second two-dimensional image include: Preprocessing the first two-dimensional image and the second two-dimensional image respectively, wherein the preprocessing includes contrast enhancement, depth conversion, binarization, and preliminary noise filtering; Performing ridge line extraction based on the preprocessed first two-dimensional image to obtain a first ridge line on the first two-dimensional image; Ridge line extraction is performed based on the preprocessed second two-dimensional image to obtain a second ridge line on the second two-dimensional image.

3. A device for acquiring annotation points of a biological sample image, the device being used to implement the method according to any one of claims 1 to 2, characterized in that: The device for acquiring annotation points of a biological sample image comprises: an acquisition module, configured to acquire a first two-dimensional image and a second two-dimensional image, wherein the first two-dimensional image and the second two-dimensional image are obtained by segmenting and projecting the three-dimensional biological sample image; a ridge line extraction module, configured to perform ridge line extraction based on the first two-dimensional image to obtain first ridge lines on the first two-dimensional image, and to perform ridge line extraction based on the second two-dimensional image to obtain second ridge lines on the second two-dimensional image, wherein each first ridge line corresponds to each second ridge line in a one-to-one manner; a superposition processing module, configured to perform superposition processing based on the first two-dimensional image and the second two-dimensional image to determine each maximum point pair, wherein the maximum point pair includes a first maximum point on the first two-dimensional image and a second maximum point corresponding to the first maximum point on the second two-dimensional image; A determination module is used to determine a target annotation point based on the first ridge line, the second ridge line, and the maximum point pair.

4. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the method for acquiring annotation points of a biological sample image according to any one of claims 1 to 2 are implemented.

5. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method for acquiring annotation points of a biological sample image according to any one of claims 1 to 2 are implemented.

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