4327 results about "Image segmentation" patented technology

The invention discloses a Mama-based spectrum dynamic fusion and double-attention enhancement medical image segmentation method, which comprises the following steps of: firstly, constructing a Mama integrated spectrum domain and attention pyramid module, fusing spectrum dynamic characteristics and a self-attention pooling mechanism, and performing frequency domain information compensation and local characteristic enhancement to obtain a spectrum dynamic fusion image; the spatial correlation loss caused by image blocking processing is relieved; secondly, designing a layered enhanced U-shaped architecture, deploying an MISAP module in a shallow layer of an encoder to capture multi-scale global context features, introducing a bipolar routing attention mechanism in a deep layer, and dynamically allocating sparse attention weights to focus a key pathological region; according to the method, the segmentation precision of complex edge textures and tiny lesions in medical images can be remarkably improved, and the Dice coefficient in breast tumor, polyp and abdominal organ segmentation tasks is averagely improved by 6.5%.

The invention relates to a medical image segmentation method and system based on residual Mama and multi-scale boundary enhancement. The method comprises the following steps: acquiring and preprocessing a medical image; inputting the image into a segmentation model based on an encoder-decoder architecture; the encoder synchronously extracts local texture features and models long-range spatial dependence through residual error convolution blocks and residual error Mama blocks which are alternately connected; fusing and enhancing the jump connection features between the encoder and the decoder through a boundary enhancement module to optimize boundary characterization; integrating a multi-scale gating attention module in a decoding path, and adaptively selecting and fusing multi-scale context features; and finally outputting the high-precision segmentation mask. The method effectively solves the problems that in the prior art, long-range dependence and local details are difficult to consider, the multi-scale feature fusion capability is insufficient, boundary segmentation is fuzzy and the like, and the segmentation accuracy, the boundary continuity and the clinical practicability are remarkably improved.

The invention provides a medical image segmentation method based on geometry and topology cooperative guidance. The medical image segmentation method comprises the following steps of image preprocessing and data enhancement; a shared encoder; a dual-path cooperative decoder; carrying out multi-mode deformation iterative refining; and a multi-objective composite loss function and an optimization strategy. The method has the beneficial effects that the performance can be remarkably improved: through a unique geometry and topology collaborative refining mechanism, the segmentation precision and the boundary definition are far superior to those in the prior art, the topology correctness of an anatomical structure can be actively maintained and repaired, clinically unacceptable errors are remarkably reduced, and the reliability of a result is improved; in addition, operation can be simplified, stability and generalization are enhanced, and advanced application is promoted.

The invention provides a mechanical arm grabbing method and system based on multi-modal information fusion, and belongs to the technical field of robot intelligent control. Comprising the steps that the conversion relation between a camera coordinate system and a mechanical arm base coordinate system is established through camera calibration, a deep learning neural network is used for conducting grabbing pose estimation on an obtained RGB-D image, and multiple candidate grabbing poses are determined; analyzing a natural language instruction input by a user based on a multi-modal large model, and recognizing a target object region from the RGB-D image by combining a target detection and image segmentation technology; based on the obtained candidate grabbing poses and the target object area, an optimal grabbing pose is screened through a scoring mechanism and mapped to a mechanical arm base coordinate system; and then a dynamic grabbing path is generated by adopting an imitation learning algorithm, and the mechanical arm is controlled to execute grabbing operation. Through multi-modal semantic understanding, accurate grabbing of the mechanical arm in a complex environment can be achieved.

The invention discloses a mobile terminal streetscape image real-time segmentation method based on a lightweight neural network, and relates to the technical field of image segmentation. The method comprises the following steps: firstly, carrying out 320 * 320 adjustment, Z-score standardization, adaptive histogram equalization and 3 * 3 Gaussian filtering preprocessing on an input streetscape image; then, an improved MobileNetV3 backbone network is used, and a five-scale feature map is output in combination with DropBlock regularization through eight feature extraction stages including depth separable convolution and an SE attention module; multi-scale features are fused through a U-shaped structure, and a fusion feature map is generated through up-sampling, element-by-element addition of dimension reduction low-layer features and an attention gating module; and during reasoning, outputting a segmentation mask by using a convolutional layer, Softmax and a conditional random field, and finally performing knowledge distillation, weight pruning, 8-bit quantization and TensorRT optimization. According to the invention, high-precision real-time street view segmentation is realized, the robustness is high, and the method is suitable for different devices and scenes.

Disclosed in the present invention are an edge-deployed semi-supervised anomaly detection method and system for a railway track foreign object. The method comprises the following steps: an edge device encoding and decoding a video stream captured by a camera to obtain an image frame sequence, and performing frame extraction; and using a semantic segmentation model to perform image segmentation on a certain image frame obtained by means of frame extraction, to obtain a railway track region segmentation image. The use of a single image as input may generate an expert model result having a high weight value; however, the determination based on a single image is not stable, multiple consecutive images of the task scene need to be inputted, the frequency of each expert model obtaining the highest weight is computed, and the expert model corresponding to the highest frequency is the final solution. The present invention supports scene-adaptive foreign object detection algorithm automatic selection, and a user can perform selection on the basis of prior knowledge, or selection may be performed by a scene-adaptive automatic algorithm selection method; the user only needs to provide a batch of image data of the current scene, and the optimal algorithm selection can be evaluated.

A clinical vertebra image segmentation method for assisting pedicle screw placement surgery, said method comprising: constructing a VerseDiff-UNet end-to-end framework, the framework being integrated with a denoising diffusion probabilistic model (DDPM); combining a noise-added image with a marked mask by using the VerseDiff-UNet framework, and guiding a diffusion direction toward a target region; and introducing a shape priors module on the basis of the DDPM, and extracting structural semantic information from an input spine image. In order to capture specific anatomical prior information in a medical image, the shape priors module is combined and the module effectively extracts the structural semantic information from the input spine image, thereby enabling more accurate anatomical structure segmentation, and facilitating accurate diagnosis and treatment of spinal disorders.

The invention relates to a medical image segmentation method based on wavelet boundary enhancement and multi-scale perception, and the method comprises the steps: firstly carrying out the preprocessing of an input medical image, including size standardization, intensity normalization and data enhancement; then, inputting the processed image into a deep fusion segmentation network, extracting high-frequency boundary features through wavelet transform and generating a boundary attention map, and capturing global context information in combination with a multi-scale dynamic sparse attention mechanism; and finally, fusing the multi-scale features through a boundary enhancement up-sampling module in a decoder stage, and optimizing a segmentation result by adopting multi-scale supervision and a mixed loss function. According to the method, the boundary precision and the detail retention capability of medical image segmentation are effectively improved, and the segmentation performance under a fuzzy boundary, a multi-scale structure and a complex background is remarkably improved.

The invention relates to the technical field of electronic component detection, in particular to an IC (integrated circuit) carrier plate detection method based on surface state image extraction, which comprises the following steps: acquiring a gray image, analyzing structural parameters, extracting gradient features, detecting boundary disturbance, integrating the image, calculating an abnormal score, identifying a defect position area, extracting features and outputting an identification result. According to the invention, by analyzing the structure parameters of the bonding pad in the gray level image, calculating the edge line segment, the center coordinate and the spacing, and constructing the two-dimensional coordinate system, the regional positioning reference is enabled to have geometric consistency, the coordinate mapping is combined with the gradient direction change frequency and the continuous aggregation point, the boundary disturbance identification precision is improved, and the image division is executed based on the disturbance region. According to the method, non-functional region mixing is effectively avoided, a clustering and probability model is introduced after region gray level statistics, a deviation scoring mechanism is constructed, gray level feature abnormity is accurately recognized, the discrimination capability of small-amplitude and low-contrast defects is improved, and the selectivity and target focusing performance of feature detection are enhanced.

The invention provides a medical image segmentation method based on an AFMHiFormer. The method comprises the steps that firstly, a multiple data enhancement module is provided, and the data distribution diversity is improved while the enhancement stability is guaranteed; secondly, a segmentation model AFHiMFormer is constructed, and the model architecture adopts a double-branch encoder and a multi-scale decoder; thirdly, a feature enhancement module is provided to construct a dynamic complementation mechanism of semantic enhancement and boundary modeling; fourthly, a multi-scale feature fusion module is introduced, multi-scale context information is captured through parallel hole convolution with different expansion rates, and self-adaptive fusion of global and local features is achieved; and fifth, a cross-scale fusion module is designed in the multi-scale decoder, so that the deep layer branch and the shallow layer branch are efficiently fused in a multi-level feature space. According to the method, the advantages of CNN and Transform are combined, dynamic fusion of local and global features is realized by providing a new module, and a remarkable performance advantage is shown in a medical image segmentation task.

The invention discloses a medical image segmentation method based on TransUNet, and belongs to the technical field of medical image segmentation. The method comprises the steps of firstly performing data preprocessing on an original image to obtain preprocessed data; and a DCA attention module is used at a jump joint, so that the problem that a semantic gap exists between characteristics of an encoder and a decoder due to the fact that a simple jump connection scheme is difficult to capture a multi-scale context is solved. The semantic difference leads to redundancy between low-level and high-level features, and finally the segmentation performance is limited. Secondly, a multi-scale boundary sensing module is added to the top layer of the encoder, so that the neural network can better segment the boundary of the target image in the training process; and inputting the preprocessed data into the improved TransUNet model to train the medical image, and outputting an image segmentation result.

The invention discloses a hydraulic engineering dam body crack detection method and system based on machine vision, and relates to the technical field of computer vision. A high-definition camera is used for shooting a dam body image, obtaining a sample data set, extracting feature parameters of different noise types, and constructing a feature database; filtering the current dam body image to obtain a first dam body image, enhancing the crack gray scale difference of the first dam body image, obtaining a second dam body image, segmenting the second dam body image, screening candidate cracks in the crack image, and identifying real cracks according to the edge features of the candidate cracks. According to the method, a feature database is constructed, a targeted dam body image processing flow is combined, a high-definition camera collection and automatic processing flow is used, filtering parameters are dynamically adjusted by constructing the feature database, gray level enhancement and precise segmentation are combined, real cracks are precisely recognized through edge feature analysis and comparison, and quantitative parameters are output; and a reliable basis is provided for dam body safety assessment.

The invention discloses a bridge structure surface crack multi-scale image segmentation and damage assessment method, and belongs to the technical field of bridge structure health monitoring, and the method comprises the steps: a multi-scale pyramid feature preprocessing step: carrying out the multi-resolution feature extraction of a bridge surface image; in the adaptive attention-guided crack segmentation step, crack region response is enhanced through a channel and space attention mechanism; the crack geometric parameter accurate quantification step is used for calculating the length, width, depth and direction of the crack; in the time sequence comparison crack development trend prediction step, the crack propagation rate is calculated according to the parameter difference value between the current detection data and the historical detection data divided by the time interval, and the development trend is predicted; in the multi-dimensional damage comprehensive evaluation step, damage scores are calculated, damage grades are determined, segmentation parameters are fed back and adjusted, and scientific data support is provided for bridge safety evaluation and maintenance decision making.

The invention discloses a 2D medical image segmentation method and system based on Mama and UNet, and the method comprises the steps: collecting and preprocessing a medical image segmentation data set, and obtaining a training set; constructing a 2D medical image segmentation model based on Mama and UNet, wherein the 2D medical image segmentation model comprises a block embedding layer, an encoder, a decoder and a prediction generation layer; designing an adaptive hierarchical loss function based on gradient statistics, and training the 2D medical image segmentation model on the training set; and inputting the medical image with segmentation into the trained model to complete image segmentation. According to the invention, the method can achieve the automatic and intelligent segmentation of the medical image through the innovative construction of the 2D medical image segmentation model based on Mamba and UNet, and is higher in segmentation accuracy and efficiency.

The invention relates to an ischemic cerebrovascular disease angiography image segmentation analysis method, which comprises the following steps: detecting gray transition abnormity, structural fracture and artifact delay signals in an angiography image, extracting negative segmentation priori points indicating a suspected ischemic area, aggregating to form a priori abnormal area, introducing a symmetric disturbance test mechanism, and analyzing the angiography image according to the priori abnormal area. Judging a potential blocked or abnormal blood vessel segment, and dynamically adjusting the segmentation threshold of the region; establishing a local interference window in the judgment region, extracting frequency and rhythm features of density stripes, and performing compensation segmentation on interrupted blood vessel segments caused by unsteady pulse change through a convolution kernel scaling strategy; calculating a texture difference value and a frequency domain response offset, if the offset is within a preset physiological tolerance range, triggering an interpolation completion mechanism, and generating a credible completion layer for subsequent calibration reference; and performing dynamic feedback adjustment and continuous calibration on the previously segmented path by analyzing the multi-path divergence degree of the vascular branch end point and the path offset change in the image sequence.

The invention discloses a highly dense broken ice image segmentation method based on an iteration MGAC and an SAM model, and the method comprises the steps: carrying out the sea ice instance segmentation of a preprocessing image based on the SAM model, and obtaining a sea ice mask image corresponding to the preprocessing image; according to the binary image and the sea ice mask image, obtaining an initial sea ice residual region image which is not identified by the SAM model; obtaining initial sea ice residual region images under different gray threshold values to obtain an initial seed mask graph; taking the initial sea ice residual region image and the initial seed mask image as inputs of a preset MGAC contour model, and obtaining an MGAC sea ice recognition result based on a multi-round iteration partitioning mechanism; and performing union operation on the MGAC sea ice identification result and the sea ice mask image to obtain a crushed ice segmentation mask result. The method solves the problem that the existing method is insufficient in structure extraction precision and boundary integrity of the dense broken ice area.

The invention relates to the field of computer technology and medical image processing, in particular to a medical image segmentation method fusing random region cutting enhancement and a pseudo label semi-supervised mechanism. In order to solve the problems of scarcity of annotation data, weak model generalization ability, inaccurate segmentation boundary and the like in a current medical image segmentation task, the invention provides a medical image segmentation method fusing random region cutting enhancement and a pseudo label semi-supervised mechanism, which is called an RCDE segmentation model. The model adopts a shared encoder and double decoder structure, combines a structure-level disturbance generation strategy, enhances the perception ability of the model for image structure change by mixing and recombining labeled images and unlabeled images, generates pseudo labels by utilizing a teacher network, and introduces a dynamic confidence coefficient screening mechanism, so that low-quality pseudo labels are effectively eliminated, and the robustness of the model is improved. The training stability and the pseudo-supervision effect are improved, preprocessing such as size normalization and image enhancement is carried out on the image, and the consistency and robustness of model input are improved.

The invention discloses a non-static scene reconstruction method and system based on multi-modal occlusion perception scoring. The method comprises the following steps: generating a geometric consistency distribution diagram, static feature points and geometric prior masks through three-dimensional reconstruction of a multi-view image; fusing the geometric prior mask and a semantic segmentation model to extract a semantic mask and a fused semantic feature map; guiding the image segmentation model to generate candidate masks based on static feature point positive point prompt and occlusion area negative frame prompt, and optimizing the consistency by using a grid complementary fusion method; constructing a multi-modal shielding scoring module, and fusing the multi-source features to output a binary static mask; and utilizing a static mask to constrain neural radiation field training, inhibiting dynamic interference and optimizing static scene reconstruction. According to the method, the static region is sensed cooperatively through multi-modal information, the robustness and accuracy of mask generation are improved, the interference of dynamic elements on neural radiation field modeling is effectively inhibited, and high-quality three-dimensional image reconstruction and new view synthesis of a non-static scene are realized.

The invention relates to the technical field of medical image processing and computer vision, in particular to a medical image segmentation method and system based on deep learning, the method is based on a U-shaped encoder-decoder architecture, a DSAB module is introduced into an encoder, and context perception of a directional anatomical structure is enhanced through complementary directional space shift and CSA mechanism weighting; an MGCF module is designed in a decoder, and a parallel multi-scale convolution path and an AGCA mechanism are combined, so that multi-level features are efficiently fused to recover boundary details. Meanwhile, links of data preprocessing, Transform structure details, segmentation result post-processing and the like are supplemented, the model performance is improved through a mixed loss function and an optimization training strategy, and the method has remarkable advantages in segmentation precision and boundary definition and provides powerful support for clinical auxiliary diagnosis.

The invention provides a medical image segmentation method and system, computer equipment and a storage medium, and belongs to the field of image processing, and the method comprises the steps: extracting preliminary features of a medical image through depth separable convolution, and splicing the preliminary features with original image residuals to obtain a preliminary feature map; after an encoder performs average pooling dimension reduction, local details and global contour features of a dimension reduction feature map are extracted by using left and right branches of a lightweight convolution module LDB, then a downsampling feature map is obtained through channel attention CA weighted fusion, and attention is calculated in combination with a self-attention mechanism module EMHA to obtain a depth feature map and a bottleneck feature map; the decoder weights the depth feature map by means of a channel and space attention to obtain a CBAM enhanced feature map, upsamples the bottleneck feature map and then splices the bottleneck feature map with the CBAM enhanced feature map, features are extracted through an LDB module, and finally a pixel-level segmentation result is output through upsampling and deconvolution, so that image segmentation achieves the effects of high quality, low complexity and low operand.

The invention provides an intelligent evaluation method and system for the antibacterial effect of a water matrix standard substance, and relates to the technical field of intelligent evaluation, and the method comprises the following steps: preparing a bacterial suspension in a logarithmic phase and checking turbidity; preparing a water matrix standard substance and a control solvent, and obtaining a baseline parameter set; setting inoculum size and incubation conditions, determining the antibacterial efficiency in parallel, and performing image segmentation correction on sticky colonies to obtain an original observation set; establishing a deviation transfer relationship, correcting the original data, and forming an endpoint quantization set; and the bacteriostatic validity is output through evidence fusion, and a supplementary experiment is carried out on a critical condition. According to the method, the accuracy and reliability of bacteriostatic effect evaluation of the water matrix standard substance are improved.

The invention discloses a medical image segmentation system based on a multi-architecture fusion and diffusion model. The medical image segmentation system comprises a dual-channel coding module which performs multi-scale feature extraction on an input medical image by using a dual-channel encoder; the dual-channel encoder comprises a plurality of scale levels, and each level comprises a first channel and a second channel which are parallel to each other; the cross-scale feature fusion module replaces the traditional jump connection, and enhances and fuses the multi-scale features output by the encoder; the decoding module receives the enhanced feature through a decoder and generates a preliminary segmentation mask; and the diffusion model post-processing module takes the preliminary segmentation mask as noise-containing input, and performs iterative denoising and optimization by using a pre-trained denoising diffusion probability model. According to the method, the limitation of a single network architecture in the aspects of long-range dependence modeling and local detail capture can be solved, a multi-scale feature fusion mechanism is optimized, and the fineness and robustness of a segmentation result can be improved.

The invention provides a medical image segmentation method and system based on spatial detail enhanced vision, and relates to the technical field of image processing. The method comprises the following steps: performing initial feature mapping on an input and preprocessed medical image to obtain an embedded feature map; the embedded feature map is input into an encoder for feature extraction, and multi-scale features are obtained; the encoder comprises a plurality of encoding stages, and the number of channels is doubled and the spatial resolution is halved through down-sampling operation between the encoding stages; the multi-scale features are subjected to up-sampling and spatial enhancement reconstruction step by step through a decoder, and a high-precision segmentation result is generated; wherein the decoder comprises a plurality of decoding stages, the decoding stages correspond to the encoding stages, and feature fusion is carried out between the corresponding stages of the encoder and the decoder through jump connection. According to the method, the boundary description precision and the segmentation robustness of the low-contrast image can be improved without increasing the linear complexity, and the method is suitable for medical image segmentation scenes of skin lesions, gastrointestinal polyps and the like.

The invention relates to the technical field of elevator image processing, in particular to an elevator image generation method, system and device based on a semantic recognition and image segmentation large model and a medium, and the method comprises the steps: obtaining an original image of an elevator detection scene and carrying out the preprocessing; the preprocessed original image is processed based on a pre-trained semantic segmentation model, and a segmentation mask graph of the elevator component is generated; respectively inputting the original image and the segmented mask image into an image encoder for feature extraction, and fusing the extracted features to generate cross-modal fusion features; based on the cross-modal fusion features, elevator part associated information is analyzed through a text generation model, and initial text description is generated; and optimizing the initial text description by utilizing a multi-source calibration mechanism, and generating a standardized elevator state document containing structured metadata. The objective of the invention is to realize automatic semantic analysis and structured report generation of elevator images.

The invention relates to the technical field of image processing, in particular to a medical image segmentation method based on multi-scale convolution bidirectional Mama. The method comprises the following steps: constructing a medical image segmentation model comprising an encoder, a decoder and a jump connection module; a medical image segmentation model is trained by using the medical image, local features are extracted through a CNN branch, and a long-range context dependency relationship is captured through a multi-scale bidirectional Mama branch; and updating the parameters of the medical image segmentation model according to the target loss, and performing medical image segmentation through the trained medical image segmentation model, thereby improving the medical image segmentation effect.

The present application provides a cardiac fibrosis diagnosis model based on multi-task attentional feature fusion. The cardiac fibrosis diagnosis model is established by the following steps: S01: image collection and labeling: obtaining cardiac magnetic resonance (MR) images as sample data, and performing manual labeling to obtain heart labels corresponding to the MR images; S02: image preprocessing, including normalization processing, data enhancement, and data clipping; S03: model establishment, including establishment of an image recovery network and establishment of an image segmentation and classification network, and executing an image recovery task; S04: model pre-training: training the image recovery network such that the encoder of the image recovery network fully learns the feature of the cardiac fibrosis image; and S05: model training. An objective of the present application is to improve the segmentation precision and diagnosis accuracy of a network model for a cardiac fibrosis image.

The invention discloses a fan blade clearance intelligent monitoring method based on multi-modal fusion. A laser radar and a visual sensor are adopted to collect point cloud data and image data of the fan blade; environment parameters are collected, validity judgment is conducted on the point cloud data and the image data of the fan blade according to the environment parameters, and radar monitoring data and visual monitoring data are obtained; modeling the fan blade according to the radar monitoring data to obtain a three-dimensional model of the fan blade, and further obtaining a radar measurement value of the blade clearance according to the three-dimensional model of the fan blade; performing image segmentation, edge extraction and depth estimation on the visual monitoring data in sequence to obtain a visual measurement value of the blade clearance; and the radar measurement value and the vision measurement value of the blade clearance are combined, and a multi-modal fusion algorithm is adopted for processing to obtain the fusion clearance distance of the fan blade. The accuracy and real-time performance of fan blade clearance monitoring are remarkably improved, blade collision accidents can be effectively prevented, and reliable guarantee is provided for safe operation of a wind turbine generator.

The invention relates to the technical field of image segmentation, in particular to a water surface floating object area image segmentation method based on edge calculation, and the method comprises the steps: analyzing a time sequence image data flow into an RGB frame sequence; suppressing high-frequency illumination noise by using a space attention mask, and extracting a target correlation feature tensor; constructing an optical path model based on a medium refractive index, resolving a theoretical characteristic offset vector and mapping the theoretical characteristic offset vector into a sampling offset field; the deformable convolution is guided to execute feature alignment reconstruction, and a geometric coherence feature map is generated; and inputting a deep network to carry out pixel division and physical mode characterization, and outputting a segmentation result in combination with fluid divergence features. According to the method, through a feature suppression and refraction reconstruction mechanism, the problem of feature topology breakage under strong light and cross-medium distortion is solved under the edge end computing power constraint, and high-fidelity region segmentation is realized.

The invention discloses an industrial image anomaly detection and segmentation method based on reinforcement learning and cross-modal alignment. The method comprises the following specific steps: S1, establishing a multi-modal large model based on reinforcement learning fine tuning; s2, inputting an image to be queried and a prompt text into the trained multi-modal large model based on reinforcement learning fine tuning, outputting a reasoning text and judging whether an abnormal answer exists, and if the answer is abnormal, entering a step S3; s3, inputting an image to be queried and the reasoning text obtained in the step S2 into a cross-modal image-text alignment module to generate layered image-text features; and S4, inputting an image to be queried into an image segmentation module, mapping the image-text features obtained in the step S3 into a dynamic prompt token through a full connection layer, inputting the dynamic prompt token into a segmentation decoder, and generating a pixel-level abnormal segmentation result. According to the invention, the accuracy of anomaly detection and the refinement degree of the segmentation result are improved.

The invention provides a hybrid CNN-Transform colon polyp image segmentation method combining edge guidance and a double attention mechanism, and is applied to the technical field of medical data processing. According to the method, a CNN encoder is adopted to extract multi-scale local features, a Transform encoder is adopted to extract global context, an edge probability graph is generated by introducing an edge guide branch based on shallow layer features, a gating coefficient is calculated according to the edge probability graph and / or statistics obtained by intermediate prediction, weighted fusion is performed on the local and global features, and the local feature and the global feature are integrated. And the decoder performs up-sampling step by step and outputs a segmentation result. Compared with the prior art, the method has the advantages in boundary integrity and small target detection. Experiments show that on a Kvasair-SEG data set, the optimal Dice of a verification set of the scheme is 0.892, the optimal HD95 of the verification set of the scheme is 12.2, the Dice of a test set of the scheme is 0.89, and the optimal HD95 of the test set of the scheme of the scheme is 12.3.