Ovarian lesion segmentation method, system, equipment and medium

Abstract

The invention discloses an ovarian lesion segmentation method, system and device and a medium, and the method comprises the steps: inputting an ultrasonic image containing an ovarian region into a coding module, and extracting a multi-scale coding feature map corresponding to the ultrasonic image through the coding module; inputting the coding feature map with the minimum scale into a Radon projection module, and outputting a fine-grained feature map through the Radon projection module; and inputting the coded feature maps except the coded feature map with the minimum scale and the fine-grained feature map into a decoding module, and outputting an ovarian lesion segmentation result through the decoding module. According to the application, after the multi-scale coding features are obtained, the Radon projection module is used for carrying out spatial feature mining on the coding feature map with the minimum scale to obtain the fine-grained features, so that the spatial features are converted; and meanwhile, the converted features keep the inherent attributes of the spatial features before conversion, namely the coding feature domain with the minimum scale, so that the loss of key information is reduced, and the accuracy of an ovarian lesion segmentation result is improved.

Description

Technical Field

[0001] The present application relates to the field of ultrasound technology, and in particular to a method, system, device and medium for segmenting ovarian lesions. Background Art

[0002] Ultrasound imaging is one of the preferred imaging technologies for screening and diagnosing ovarian lesions due to its advantages of being non-invasive, radiation-free, real-time, and low-cost. Ultrasound doctors' identification of ovarian lesions in ultrasound images is highly dependent on clinical experience and there are subjective differences; the quality of ultrasound images also affects the identification of lesions to a certain extent, especially the difficulty in accurately judging ovarian lesions with blurred boundaries.

[0003] With the development of artificial intelligence, deep learning-based technologies have been widely studied in the field of medical image analysis and have achieved remarkable results, which is expected to reduce the clinical pressure of doctors. Although there are methods that use deep learning technology to achieve the segmentation of ovarian lesions in ultrasound images, the existing methods basically stack deep learning modules to build neural networks to process and learn images, ignoring the mining of unique information through the transformation of feature space, which affects the accuracy of ovarian lesion segmentation.

[0004] Therefore the prior art still needs to be improved and enhanced. Summary of the invention

[0005] The technical problem to be solved by the present application is to provide a method, system, device and medium for segmenting ovarian lesions in view of the deficiencies of the existing technology.

[0006] In order to solve the above technical problems, the first aspect of the present application provides a method for segmenting ovarian lesions, wherein a trained ovarian lesion segmentation model is applied, and the ovarian lesion segmentation model includes an encoding module, a Radon projection module and a decoding module; the ovarian lesion segmentation method specifically includes: Inputting an ultrasound image containing an ovarian region into a coding module, and extracting a multi-scale coding feature map corresponding to the ultrasound image through the coding module; Inputting the smallest-scale coded feature map among the multi-scale coded feature maps into the Radon projection module, and outputting a fine-grained feature map through the Radon projection module; The encoding feature maps except the encoding feature map with the smallest scale and the fine-grained feature map are input into the decoding module, and the ovarian lesion segmentation result is output through the decoding module.

[0007] The ovarian lesion segmentation method, wherein the Radon projection module includes a latent feature projection unit, a feature extraction unit and a back projection unit; the output of the fine-grained feature map through the Radon projection module specifically includes: The ovarian lesion segmentation method, wherein the Radon projection module includes a latent feature projection unit, a feature extraction unit and a back projection unit; the output of the fine-grained feature map through the Radon projection module specifically includes: The encoding feature map with the smallest scale is subjected to Radon transformation at a preset angle by the latent feature projection unit to obtain a mapping feature map, wherein the mapping feature map includes a channel dimension, an angle dimension and a width dimension; Performing feature extraction on the mapping feature map from a channel dimension and an angle dimension by the feature extraction unit to output a channel feature map and an angle feature map; The channel feature map and the angle feature map are subjected to a reverse Radon transform by the reverse projection unit to output a fine-grained feature map.

[0008] The ovarian lesion segmentation method, wherein the preset angle includes multiple preset angles, and the step of performing Radon transformation on the smallest-scale coded feature map at the preset angle by the latent feature projection unit to obtain a mapping feature map specifically includes: The encoding feature map with the smallest scale is subjected to Radon transformation at each preset angle by the latent feature projection unit to obtain a mapping feature map.

[0009] The method for segmenting ovarian lesions, wherein the angle dimension of the mapping feature graph is equal to the number of preset angles.

[0010] The ovarian lesion segmentation method, wherein the feature extraction unit extracts features from the channel dimension and the angle dimension of the mapping feature map to output a channel feature map and an angle feature map specifically includes: Splitting the mapping feature map into a plurality of channel dimension features according to the channel dimension and into a plurality of angle dimension features according to the angle dimension by the feature extraction unit; Input several angle dimension features into the transformer, and determine the angle feature map through the transformer; Several channel dimension features are input into the transformer, and the dimension feature map is determined by the transformer.

[0011] The ovarian lesion segmentation method, wherein the performing of a reverse Radon transform on the channel feature map and the angle feature map by the reverse projection unit to output a fine-grained feature map specifically includes: Performing reverse Radon transformation on the channel feature map and the angle feature map respectively through the reverse projection unit to obtain a reverse channel feature map and a reverse angle feature map; A fine-grained feature map is determined by the reverse projection unit based on the reverse channel feature map, the reverse angle feature map and the encoding feature map with the smallest scale.

[0012] The ovarian lesion segmentation method, wherein the determining of the fine-grained feature map by the reverse projection unit based on the reverse channel feature map, the reverse angle feature map and the encoding feature map with the smallest scale specifically includes: Cascading along the channel dimension based on the reverse channel feature map, the reverse angle feature map and the encoding feature map with the smallest scale by the reverse projection unit to form a cascade feature map; The cascade feature map is convolved by the back projection unit to obtain a fine-grained feature map, and the image scale of the fine-grained feature map is the same as the image scale of the smallest-scale encoded feature map.

[0013] The second aspect of the present application provides an ovarian lesion segmentation system, which uses a trained ovarian lesion segmentation model, wherein the ovarian lesion segmentation model includes an encoding module, a Radon projection module, and a decoding module; the ovarian lesion segmentation system specifically includes: An acquisition module, used for acquiring an ultrasound image including an ovarian region; A control module is used to input an ultrasound image containing an ovarian area into a coding module, and extract a multi-scale coding feature map corresponding to the ultrasound image through the coding module; input a coding feature map with the smallest scale in the multi-scale coding feature map into the Radon projection module, and output a fine-grained feature map through the Radon projection module; and input each coding feature map except the coding feature map with the smallest scale and the fine-grained feature map into the decoding module, and output an ovarian lesion segmentation result through the decoding module.

[0014] A third aspect of the present application provides a computer-readable storage medium, which stores one or more programs. The one or more programs can be executed by one or more processors to implement the steps in any of the above-mentioned methods for segmenting ovarian lesions.

[0015] A fourth aspect of the present application provides a terminal device, comprising: a processor and a memory; The memory stores a computer-readable program executable by the processor; When the processor executes the computer-readable program, the processor implements the steps in any of the above-mentioned methods for segmenting ovarian lesions.

[0016] Beneficial effects: Compared with the prior art, the present application provides a method, system, device and medium for segmenting ovarian lesions, the method comprising inputting an ultrasound image containing an ovarian region into a coding module, extracting a multi-scale coding feature map corresponding to the ultrasound image through the coding module; inputting the coding feature map with the smallest scale in the multi-scale coding feature map into the Radon projection module, outputting a fine-grained feature map through the Radon projection module; inputting each coding feature map except the coding feature map with the smallest scale and the fine-grained feature map into the decoding module, and outputting the ovarian lesion segmentation result through the decoding module. After obtaining the multi-scale coding features through the coding module, the present application performs spatial feature mining on the coding feature map with the smallest scale through the Radon projection module to obtain fine-grained features. Such an operation not only converts the spatial features, but also the converted features retain the inherent properties of the spatial features before the conversion, i.e., the coding feature domain with the smallest scale, which reduces the loss of key information and is conducive to improving the accuracy of the ovarian lesion segmentation results. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0018] Figure 1 Block diagram of the working principle of the ovarian lesion segmentation model.

[0019] Figure 2 This is a flow chart of a method for segmenting ovarian lesions provided in an embodiment of the present application.

[0020] Figure 3 This is the structural block diagram of the Radon projection module.

[0021] Figure 4 This is a principle block diagram of the ovarian lesion segmentation system provided in an embodiment of the present application.

[0022] Figure 5 A functional block diagram of a terminal device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0023] The embodiments of the present application provide a method, system, device and medium for segmenting ovarian lesions. In order to make the purpose, technical solution and effect of the present application clearer and more specific, the present application is further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described here are only used to explain the present application and are not used to limit the present application.

[0024] It will be understood by those skilled in the art that, unless expressly stated, the singular forms "one", "said", and "the" used herein may also include plural forms. It should be further understood that the term "comprising" used in the specification of the present application refers to the presence of the features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof. It should be understood that when we refer to an element as being "connected" or "coupled" to another element, it may be directly connected or coupled to the other element, or there may be an intermediate element. In addition, the "connection" or "coupling" used herein may include wireless connection or wireless coupling. The term "and / or" used herein includes all or any unit and all combinations of one or more associated listed items.

[0025] It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as those generally understood by those skilled in the art to which this application belongs. It should also be understood that terms such as those defined in general dictionaries should be understood to have meanings consistent with those in the context of the prior art, and will not be interpreted with idealized or overly formal meanings unless specifically defined as here.

[0026] It should be understood that the sequence numbers and sizes of the steps in this embodiment do not mean the order of execution. The execution order of each process is determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present application.

[0027] The application content is further explained below through the description of embodiments in conjunction with the accompanying drawings.

[0028] This embodiment provides a method for segmenting ovarian lesions, using a trained ovarian lesion segmentation model, such as Figure 1 As shown, the ovarian lesion segmentation model includes an encoding module, a Radon projection module and a decoding module, wherein the Radon projection module is located between the encoding module and the decoding module and is connected to the encoding module and the decoding module respectively, wherein the encoding module is used to extract features from the ultrasound image to obtain a multi-scale encoding feature map, the Radon projection module is used to perform spatial learning on the encoding feature map with the smallest scale to obtain a fine-grained feature map, and the decoding module is used to decode based on the fine-grained feature map and the encoding feature map except the encoding feature map with the smallest scale to obtain a decoding feature map, and determine the ovarian lesion segmentation result based on the decoding feature map. The embodiment of the present application embeds the Radon projection module in the U-structured network, and performs spatial feature learning through the Radon projection module while retaining the integrity of the source domain information to enrich the feature information included in the encoding feature, thereby improving the accuracy of the ovarian lesion segmentation result.

[0029] like Figure 2 As shown, the ovarian lesion segmentation method provided in the embodiment of the present application specifically includes: S10, inputting an ultrasound image containing an ovarian region into a coding module, and extracting a multi-scale coding feature map corresponding to the ultrasound image through the coding module.

[0030] Specifically, the ultrasound image may be a real-time ultrasound image collected in real time by an ultrasound device, an offline ultrasound image stored offline, or a remote ultrasound image sent by other external devices, etc. The ultrasound image includes the ovarian region, that is, the ultrasound image is obtained by performing an ovarian ultrasound examination on the patient, that is, before the ultrasound image including the ovarian region is input into the encoding module, the ultrasound image including the ovarian region is first obtained, and the obtained ultrasound image including the ovarian region may also be pre-processed, for example, clarity detection, ovarian region detection, noise removal, etc.

[0031] After obtaining the ultrasound image containing the ovarian region, the ultrasound image containing the ovarian region is input into the encoding module, and the feature extraction of the ultrasound image containing the ovarian region is performed by the encoding module, wherein the encoding module includes a plurality of encoding units, the plurality of encoding units are cascaded in sequence, and each encoding unit extracts a coded feature map of a certain scale. For example, assuming that the image size of the ultrasound image is , Indicates the image height, Indicates the image width, such as Figure 1 As shown, the encoding module includes five encoding units. The image scales of the encoding feature maps extracted by each encoding unit in the cascade order are (48, 1), (96, 1 / 2), (192, 1 / 4), (384, 1 / 8), and (768, 1 / 16), where: Indicates that the image width of the coded feature map is the image width of the ultrasound image , the image height of the encoded feature map is the image height of the ultrasound image (For example, 1 / 2 means the image height and image width of the encoded feature map are ).

[0032] It should be noted that, in practical applications, the number of coding units included in the coding module and the image scale of the coding feature map extracted by each coding unit can be set according to actual needs, and no specific restrictions are made here.

[0033] S20, inputting the smallest-scale coding feature map among the multi-scale coding feature maps into the Radon projection module, and outputting a fine-grained feature map through the Radon projection module.

[0034] Specifically, the Radon projection module is used to learn the spatial features of the smallest-scale coded feature map, so that the fine-grained feature map outputted by it contains geometric structure information, thereby improving the accuracy of the ovarian lesion segmentation result determined by decoding the fine-grained feature map. The Radon projection module is configured with Radon transform and inverse Radon transform, which retains the complete information of the smallest-scale coded feature map while learning the spatial features through Radon transform.

[0035] In one implementation, if Figure 3 As shown, the Radon projection module includes a hidden feature projection unit, a feature extraction unit and a reverse projection unit; the hidden feature projection unit is connected to the feature extraction unit, the feature extraction unit is connected to the reverse projection unit, the hidden feature projection unit is used to perform Radon transform to obtain a mapping feature map; the feature extraction unit is used to extract features from the channel dimension and the angle dimension of the mapping feature map to obtain a channel feature map and an angle feature map; the reverse projection unit is used to perform reverse Radon transform on the channel feature map and the angle feature map to obtain a fine-grained feature map. Based on this, the output of the fine-grained feature map through the Radon projection module specifically includes: S21, performing Radon transformation on the smallest-scale encoding feature map at a preset angle through the latent feature projection unit to obtain a mapping feature map; S22, performing feature extraction on the mapping feature map from a channel dimension and an angle dimension by the feature extraction unit to output a channel feature map and an angle feature map; S23. Perform a reverse Radon transform on the channel feature map and the angle feature map through the reverse projection unit to output a fine-grained feature map.

[0036] Specifically, in step S21, the mapping feature map is obtained by performing a Radon transform on the smallest-scale coded feature map at a preset angle. That is to say, compared with the existing Radon transform at the image level, the embodiment of the present application implements the Radon transform at the feature level, so that the expression form of the feature can be intervened in the feature learning process, and fine-grained features can be learned, thereby improving the accuracy of the ovarian lesion segmentation results. At the same time, after the present application implements the Radon transform at the feature level, it will also perform a reverse Radon transform on the learned features to back-project the features back to the spatial dimension of the smallest-scale coded feature map.

[0037] Furthermore, the preset angle is pre-set and is used to determine the projection direction of the encoding feature map with the smallest scale, wherein the preset angle can be multiple, for example, 0°, 30°, 60°, 90°, 120°, 150° and 180°.

[0038] In addition, the preset angle can be set according to the ultrasound image or user needs. For example, interactive information can be obtained by interacting with the user. The interactive information may include multiple preset angles or the angle interval between two adjacent preset angles (such as 15°, 30°, etc.). The preset angle is then determined according to the interactive information, and the preset angle is configured in the latent feature projection unit, so that the latent feature projection unit can map the smallest-scale encoded feature map at the preset angle to obtain a mapping feature map.

[0039] Exemplarily, the step of performing a Radon transform on the smallest-scale encoded feature map at a preset angle by the latent feature projection unit to obtain a mapping feature map specifically includes: The encoding feature map with the smallest scale is subjected to Radon transformation at each preset angle by the latent feature projection unit to obtain a mapping feature map.

[0040] Specifically, the mapping feature map is formed by accumulating the projections corresponding to each preset angle, and the projection corresponding to each preset angle is obtained by performing a Radon transform on the preset angle of the smallest-scale coding feature map. The mapping feature map includes a channel dimension, an image width dimension, and an angle dimension. The image width of the mapping feature map is the same as the image width of the smallest-scale coding feature map, the number of channels of the mapping feature map is the same as the number of channels of the smallest-scale coding feature map, and the image height of the mapping feature map is equal to the number of preset angles. For example, Figure 3 As shown, the number of channels of the smallest-scale encoding feature map is , the image width of the smallest scale encoding feature map is , the preset number of angles is , then the image scale of the mapped feature map is .

[0041] Further, in step S22, after the mapping feature map is obtained, feature extraction can be performed on the mapping feature map to extract finer-grained features. When feature extraction is performed on the mapping feature map, feature extraction is performed from the angle dimension and the channel dimension respectively to extract richer spatial feature information.

[0042] Exemplarily, the extracting features of the mapping feature map from the channel dimension and the angle dimension by the feature extraction unit to output the channel feature map and the angle feature map specifically includes: Splitting the mapping feature map into a plurality of channel dimension features according to the channel dimension and into a plurality of angle dimension features according to the angle dimension by the feature extraction unit; Input several angle dimension features into the transformer, and determine the angle feature map through the transformer; Several channel dimension features are input into the transformer, and the channel feature map is determined by the transformer.

[0043] Specifically, the number of channel dimension features is equal to the number of channels of the mapping feature map, and the number of angle dimension features is equal to the preset number of angles corresponding to the mapping feature map. Of course, in practical applications, the mapping feature map can also be split in other ways, for example, the channel dimension and angle dimension of the mapping feature map are obtained, and then the channel dimension of each channel dimension feature is determined according to the obtained channel dimension, and the angle dimension of each angle dimension feature is determined according to the angle dimension. Specifically, the correspondence between the channel dimension and the channel splitting cardinality, and the correspondence between the angle dimension and the angle splitting cardinality can be preset. After the channel dimension and the angle dimension of the mapping feature map are obtained, the channel splitting cardinality and the angle splitting cardinality corresponding to the mapping feature map are determined according to the two preset correspondences, and then the mapping feature map is split according to the channel splitting cardinality and the angle splitting cardinality, respectively, so that the number of channels of the channel dimension feature obtained by splitting is equal to the channel splitting cardinality, and the angle dimension of the angle dimension feature is equal to the angle splitting cardinality, wherein, when the number of channels of the last channel dimension feature can be less than the channel splitting cardinality, the angle dimension of the last angle dimension feature can be less than the angle splitting cardinality.

[0044] Furthermore, after obtaining a number of channel dimension features and a number of angle dimension features, the channel dimension features and the angle dimension features can be respectively input into a transformer, and the transformer is used to extract features of the channel dimension features and the angle dimension features, respectively, to obtain a channel feature map and an angle feature map. Specifically, after splitting to obtain a number of channel dimension features, the channel dimension features are input into a transformer, and the transformer outputs the channel features corresponding to each channel dimension feature, and then all the channel features are cascaded according to the channel dimension to obtain a channel feature map. Similarly, after splitting to obtain a number of angle dimension features, the angle dimension features are input into a transformer, and the transformer outputs the angle features corresponding to each angle dimension feature, and then all the angle features are cascaded according to the angle dimension to obtain an angle feature map. Among them, the image scales of the channel feature map and the angle feature map are the same as the image scale of the mapping feature map. In addition, the transformer can adopt a Transformer network, which includes multi-head attention, layer normalization, multi-layer perceptron and layer normalization cascaded in sequence, and the input item of the multi-head attention is fused with the output item of the multi-head attention through an adder as the input item of the layer normalization of the multi-head attention, and the input item of the multi-layer perceptron is fused with the output item of the multi-layer perceptron through an adder as the input item of the layer normalization of the multi-layer perceptron.

[0045] Further, in step S23, after obtaining the channel feature map and the angle feature map, the channel feature map and the angle feature map can be respectively subjected to reverse Radon transformation by a reverse projection unit to restore the channel feature map and the angle feature map to spatial features having the same size as the smallest-scale encoding feature map. Exemplarily, the reverse Radon transformation of the channel feature map and the angle feature map by the reverse projection unit to output a fine-grained feature map specifically includes: Performing reverse Radon transformation on the channel feature map and the angle feature map respectively through the reverse projection unit to obtain a reverse channel feature map and a reverse angle feature map; The reverse channel feature map, the reverse angle feature map and the smallest-scale encoding feature map are cascaded through the reverse projection unit to obtain a fine-grained feature map.

[0046] Specifically, Figure 3 As shown, the reverse projection unit performs a reverse Radon transform on the channel feature map and the angle feature map to transform the channel feature map into a reverse channel feature map, and transforms the angle feature map into a reverse angle feature map, wherein the angle for implementing the reverse Radon transform is the same as the angle for implementing the Radon transform, that is, the channel feature map and the angle feature map are respectively reversed Radon transformed according to the preset angle corresponding to the Radon transform.

[0047] Further, after obtaining the channel feature map and the angle feature map, the reverse channel feature map, the reverse angle feature map and the smallest-scale encoding feature map are cascaded according to the channel dimension, and a convolution operation is performed on the cascaded feature map to obtain a fine-grained feature map. The image scale of the fine-grained feature map is the same as the image scale of the smallest-scale encoding feature map.

[0048] S30, inputting each coded feature map except the coded feature map with the smallest scale and the fine-grained feature map into the decoding module, and outputting the ovarian lesion segmentation result through the decoding module.

[0049] Specifically, the fine-grained feature map is used to compare the encoded features of other scales except the smallest encoded feature map. Figure 1 The decoded features are input into the decoding module, and the features are gradually restored to the size of the ultrasound image. Then, the ovarian lesion segmentation result is determined based on the decoded features output by the last decoding unit in the decoding module. For example, the decoded features output by the last decoding module are input into the sigmoid activation function to obtain a probability map, and the probability map is converted into the probability map by taking the threshold. Figure 2 The ovarian lesion segmentation result can be a contour segmentation map of the lesion area, or a boundary box of the lesion area.

[0050] In summary, the present embodiment provides a method, system, device and medium for segmenting ovarian lesions, the method comprising inputting an ultrasound image containing an ovarian region into a coding module, extracting a multi-scale coding feature map corresponding to the ultrasound image through the coding module; inputting the coding feature map with the smallest scale in the multi-scale coding feature map into the Radon projection module, outputting a fine-grained feature map through the Radon projection module; inputting each coding feature map except the coding feature map with the smallest scale and the fine-grained feature map into the decoding module, and outputting the ovarian lesion segmentation result through the decoding module. After obtaining the multi-scale coding features through the coding module, the present application performs spatial feature mining on the coding feature map with the smallest scale through the Radon projection module to obtain fine-grained features, so that not only the spatial feature information can be learned, but also the fine-grained features can retain the integrity of the coding feature domain information with the smallest scale, which can significantly improve the accuracy of the ovarian lesion segmentation results.

[0051] Based on the above-mentioned ovarian lesion segmentation method, this embodiment provides an ovarian lesion segmentation system, which uses a trained ovarian lesion segmentation model, wherein the ovarian lesion segmentation model includes an encoding module, a Radon projection module, and a decoding module; Figure 4 As shown, the ovarian lesion segmentation system specifically includes: An acquisition module 100 is used to acquire an ultrasound image including an ovarian region; The control module 200 is used to input an ultrasound image containing an ovarian area into a coding module, and extract a multi-scale coding feature map corresponding to the ultrasound image through the coding module; input the coding feature map with the smallest scale in the multi-scale coding feature map into the Radon projection module, and output a fine-grained feature map through the Radon projection module; and input each coding feature map except the coding feature map with the smallest scale and the fine-grained feature map into the decoding module, and output the ovarian lesion segmentation result through the decoding module.

[0052] Based on the above-mentioned method for segmenting ovarian lesions, this embodiment provides a computer-readable storage medium, which stores one or more programs. The one or more programs can be executed by one or more processors to implement the steps in the method for segmenting ovarian lesions as described in the above-mentioned embodiment.

[0053] Based on the above ovarian lesion segmentation method, the present application also provides a terminal device, such as Figure 5As shown, it includes at least one processor 20; a display screen 21; and a memory 22, and may also include a communications interface 23 and a bus 24. The processor 20, the display screen 21, the memory 22, and the communications interface 23 may communicate with each other through the bus 24. The display screen 21 is configured to display a preset user guide interface in the initial setting mode. The communications interface 23 may transmit information. The processor 20 may call the logic instructions in the memory 22 to execute the method in the above embodiment.

[0054] In addition, the logic instructions in the memory 22 can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product.

[0055] The memory 22 is a computer-readable storage medium that can be configured to store software programs, computer executable programs, such as program instructions or modules corresponding to the methods in the embodiments of the present disclosure. The processor 20 executes functional applications and data processing by running the software programs, instructions or modules stored in the memory 22, that is, implementing the methods in the above embodiments.

[0056] The memory 22 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and at least one application required for a function; the data storage area may store data created according to the use of the terminal device, etc. In addition, the memory 22 may include a high-speed random access memory and may also include a non-volatile memory. For example, a variety of media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk, may also be a transient storage medium.

[0057] In addition, the specific process of loading and executing the multiple instruction processors in the above-mentioned storage medium and the terminal device has been described in detail in the above-mentioned method, and will not be described one by one here.

[0058] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application 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. However, 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 embodiments of the present application.

Claims

1. A method for segmenting ovarian lesions, characterized in that: A trained ovarian lesion segmentation model is applied, wherein the ovarian lesion segmentation model includes an encoding module, a Radon projection module, and a decoding module; and the ovarian lesion segmentation method specifically includes: Inputting an ultrasound image containing an ovarian region into a coding module, and extracting a multi-scale coding feature map corresponding to the ultrasound image through the coding module; Inputting the smallest-scale coded feature map among the multi-scale coded feature maps into the Radon projection module, and outputting a fine-grained feature map through the Radon projection module; The encoding feature maps except the encoding feature map with the smallest scale and the fine-grained feature map are input into the decoding module, and the ovarian lesion segmentation result is output through the decoding module.

2. The method for segmenting ovarian lesions according to claim 1, characterized in that: The Radon projection module includes a latent feature projection unit, a feature extraction unit and a back projection unit; the output of the fine-grained feature map through the Radon projection module specifically includes: The encoding feature map with the smallest scale is subjected to Radon transformation at a preset angle by the latent feature projection unit to obtain a mapping feature map, wherein the mapping feature map includes a channel dimension, an angle dimension and a width dimension; The feature extraction unit performs feature extraction on the mapping feature map from a channel dimension and an angle dimension respectively to output a channel feature map and an angle feature map; The channel feature map and the angle feature map are subjected to a reverse Radon transform by the reverse projection unit to output a fine-grained feature map.

3. The method for segmenting ovarian lesions according to claim 2, characterized in that: The preset angle includes a plurality of preset angles, and the step of performing Radon transformation on the smallest-scale encoding feature map at the preset angle by the latent feature projection unit to obtain a mapping feature map specifically includes: The encoding feature map with the smallest scale is subjected to Radon transformation at each preset angle by the latent feature projection unit to obtain a mapping feature map.

4. The method for segmenting ovarian lesions according to claim 3, characterized in that: The angular dimension of the mapping feature map is equal to the number of the preset angles.

5. The method for segmenting ovarian lesions according to claim 2, characterized in that: The extracting features of the mapping feature map from the channel dimension and the angle dimension by the feature extraction unit to output the channel feature map and the angle feature map specifically includes: Splitting the mapping feature map into a plurality of channel dimension features according to the channel dimension and into a plurality of angle dimension features according to the angle dimension by the feature extraction unit; Input several angle dimension features into the transformer, and determine the angle feature map through the transformer; Several channel dimension features are input into the transformer, and the dimension feature map is determined by the transformer.

6. The method for segmenting ovarian lesions according to claim 2, characterized in that: The performing a reverse Radon transform on the channel feature map and the angle feature map by the reverse projection unit to output a fine-grained feature map specifically includes: Performing reverse Radon transformation on the channel feature map and the angle feature map respectively through the reverse projection unit to obtain a reverse channel feature map and a reverse angle feature map; A fine-grained feature map is determined by the reverse projection unit based on the reverse channel feature map, the reverse angle feature map and the encoding feature map with the smallest scale.

7. The method for segmenting ovarian lesions according to claim 6, characterized in that: The determining of the fine-grained feature map based on the reverse channel feature map, the reverse angle feature map and the minimum-scale encoding feature map by the reverse projection unit specifically includes: Cascading along the channel dimension based on the reverse channel feature map, the reverse angle feature map and the encoding feature map with the smallest scale by the reverse projection unit to form a cascade feature map; The cascade feature map is convolved by the back projection unit to obtain a fine-grained feature map, and the image scale of the fine-grained feature map is the same as the image scale of the smallest-scale encoded feature map.

8. A segmentation system for ovarian lesions, characterized in that: A trained ovarian lesion segmentation model is applied, wherein the ovarian lesion segmentation model includes an encoding module, a Radon projection module, and a decoding module; the ovarian lesion segmentation system specifically includes: An acquisition module, used for acquiring an ultrasound image including an ovarian region; A control module is used to input an ultrasound image containing an ovarian area into a coding module, and extract a multi-scale coding feature map corresponding to the ultrasound image through the coding module; input a coding feature map with the smallest scale in the multi-scale coding feature map into the Radon projection module, and output a fine-grained feature map through the Radon projection module; and input each coding feature map except the coding feature map with the smallest scale and the fine-grained feature map into the decoding module, and output an ovarian lesion segmentation result through the decoding module.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the steps in the method for segmenting ovarian lesions according to any one of claims 1-7.

10. A terminal device, characterized in that: include: Processor and memory; The memory stores a computer-readable program executable by the processor; When the processor executes the computer-readable program, the processor implements the steps in the method for segmenting ovarian lesions according to any one of claims 1 to 7.

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