699results about How to "Improve Segmentation Accuracy" patented technology

The invention provides an abdomen multi-organ image segmentation method fusing multi-scale features, and the method comprises the steps: S1, constructing a space detail fusion module extraction network, which comprises a space attention module, a channel attention module, an up-sampling operation, a down-sampling operation and a feature smoothing operation; s2, designing an encoder, and connecting the encoder with the space detail fusion module; s3, transmitting a plurality of features with different resolutions obtained by the encoder to a decoder through jump connection for information recovery; and S4, arranging the abdominal multi-organ medical image data set to perform model training, and performing multi-organ segmentation prediction on the abdominal multi-organ medical image. According to the method, local and global information is fully utilized, multi-scale feature information is combined, the image feature extraction and expression capability of the model is improved, and the segmentation precision of multiple organs of the abdomen is improved.

The invention provides a tumor area double-view detection method and system based on image modalities and views, and relates to the field of medical image analys.The method comprises the steps that multi-modal image data are collected from the body of a patient, and image data of different view angles at the same position are obtained according to time frames; extracting multi-modal features from the image data of different modalities and different view angles, and performing weighted fusion on the extracted multi-modal features to form a fused feature map; inputting the fused feature map to train and predict the 3D-CNN network so as to identify and position a tumor region, and marking the tumor region by using a mask output by the segmentation model; calculating the pixel number of the tumor area in each layer of image according to the marked mask, and multiplying the pixel number by the volume of each pixel to obtain the volume of the tumor area; and the segmentation results of each layer of image are superposed, and a three-dimensional model of a tumor region is generated by using 3D reconstruction, so that the limitation of single-mode and single-view image data in tumor detection is overcome.

The invention discloses a semi-supervised medical image segmentation method based on eye movement guide mixed data enhancement. The method comprises the following steps: collecting fixation position information of a doctor when reading a medical image through eye movement tracking equipment; using a teacher-student network model and adopting a two-stage training mode to train the model; the method comprises the following steps: taking GazeMix as a data enhancement strategy in training, extracting a fixation area in a labeled or unlabeled image, and pasting the fixation area to the background of other unlabeled images to generate a mixed image data pair; in the first stage, a GazeMix data enhancement strategy is combined to pre-train a teacher network; in the second stage, a student network is trained, GazeMix generates diversified data, and meanwhile, a teacher network is pre-trained for pseudo tag generation; multi-scale gaze perception is adopted, and model perception information is extracted on different scales and used for perception loss calculation; and the optimizer iteratively optimizes the student network parameters, and updates the teacher network by using an index moving average method.

The invention discloses a lightweight semantic segmentation network and method for power grid inspection, and the method comprises the steps: employing an encoder-decoder architecture, inputting original data into a lightweight MobileNet v2 backbone network for feature extraction at an encoder part, inputting an obtained feature map into a lightweight large-scale ASPP module, and carrying out the feature extraction; further performing multi-scale feature extraction on the high-dimensional feature map; and in the decoder part, high-dimensional features and low-dimensional features obtained by the backbone network are respectively input into a cross attention wavelet fusion module, after an obtained feature map is fused with the output of a decoder, the feature map is recovered to the size of original data through down-sampling operation by a full-dimensional dynamic convolution post-processing module, and a final prediction mask is obtained. According to the invention, by optimizing a coding and decoding structure, the receptive field of the model and the modeling capability of a narrow and long target are improved, the balance between the precision and the complexity can be effectively realized, and the method is suitable for an unmanned aerial vehicle inspection system with limited resources.

The invention relates to the technical field of image processing, and discloses a scoliosis angle intelligent measurement method and system based on spine CT three-dimensional reconstruction. The method comprises the following steps: carrying out adaptive threshold segmentation and gray optimization processing on a spine CT sequence image to obtain a standardized preprocessed image, and carrying out adaptive bone tissue region identification and boundary enhancement processing to obtain spine skeleton segmentation data; performing three-dimensional grid construction and curved surface smoothing processing on the spine skeleton segmentation data to obtain spine three-dimensional model data; carrying out centrum gap identification and key point positioning processing on the spine three-dimensional model data to obtain a centrum feature data set, and carrying out spine midline curve construction and end centrum marking processing to obtain Cobb angle data; and performing three-dimensional space reconstruction and multi-plane projection processing on the Cobb angle data to obtain a measurement result report. According to the method and the device, full-automatic processing from acquisition of the CT image to generation of the final measurement result report is realized on the premise of ensuring the measurement accuracy.

The embodiment of the invention provides an image semantic segmentation method and device fused with Fourier transform, and the method comprises the steps: constructing a segmentation model based on a U-shaped network, and carrying out the feature extraction through employing a four-layer convolution pooling network as an encoder; constructing a Fourier transform module, performing two-dimensional discrete Fourier transform on the input image to obtain a spectrogram, and performing amplitude spectrum and phase spectrum decomposition on the spectrogram to obtain an initial frequency domain feature; performing preset size scaling and pixel normalization processing on the input image to obtain a pre-processed image, and inputting the pre-processed image into a Fourier transform module to obtain four groups of adjusted frequency domain features; a bilinear interpolation algorithm is adopted to adjust the size of the prediction probability graph to the size of an original input image, pixel points with probability values larger than 0.5 in the prediction probability graph are marked as foreground areas, and a final semantic segmentation result is obtained; according to the method, the semantic segmentation task in a complex scene can be better processed, and the segmentation precision and the model robustness are improved.

The invention relates to the technical field of prostate MRI (Magnetic Resonance Imaging) image segmentation, in particular to a prostate MRI image segmentation method based on Mamb-Unet, which realizes effective fusion of features of different scales between an encoder and a decoder, and fuses multi-scale information on a space axis by using a space attention bridge segment to fuse fusion features of different levels from the encoder, so that the segmentation accuracy of the prostate MRI image is improved. And then global and local information is captured through 1D convolution and full connection in the channel attention bridge segment, so that a channel attention map is generated, channel information is better integrated, and multi-level different-scale feature fusion is realized.

The invention discloses a brain tumor MRI image segmentation model and method based on an improved Swin UNETR network, and belongs to the technical field of image segmentation. The brain tumor MRI image segmentation model is obtained by improving a Swin UNETR as a baseline model and comprises an encoder, a jump connection and a decoder, the decoder comprises a plurality of convolution fusion modules and an attention mechanism module, feature information of different levels is fused in the decoder by using the plurality of convolution fusion modules, and the attention mechanism module is used for identifying the feature information of the different levels of the feature information of the different levels of the feature information of the different levels of the feature information of the different levels of the feature information of the different levels of the convolution fusion modules. And the attention mechanism module is used to focus the feature information obtained by the plurality of convolutions. By adopting the scheme of the invention, the problem that the existing medical image segmentation technology based on deep learning is insufficient in brain tumor MRI image segmentation precision and is difficult to realize accurate segmentation especially in the face of different types of tumors and variability can be effectively solved, the calculation amount is small, and the image segmentation efficiency can be effectively improved.

The invention relates to the technical field of image processing, in particular to a machine vision-based cover film surface defect detection method, which comprises the following steps of: firstly acquiring a cover film surface image, iteratively updating a segmentation threshold value for multiple times according to an Otsu algorithm, segmenting a scratch and a normal area by using the current threshold value in each iteration updating process, clustering pixel points of the scratch area, and obtaining a target image; a scratch segmentation coefficient is calculated according to the difference between a pixel point gray average value of each cluster and a standard gray value and the average Euclidean distance from the pixel point to the fitting curve, a current threshold segmentation effect quantized value is calculated by combining the difference between a scratch area and a normal area, and after iteration is completed, a threshold corresponding to the maximum quantized value is selected as the optimal threshold. And segmenting the image by using the optimal threshold value to determine a scratch defect detection result. According to the method, the process of calculating the segmentation threshold is optimized, the noise interference is reduced, the segmentation requirements of defects such as scratches are better met, the segmentation accuracy is improved, and the scratch defects on the surface of the cover film can be effectively identified.

The invention relates to a tumor image segmentation method. Performing multi-scale feature extraction on the image by using a convolutional neural network (CNN) to capture different-scale information of the tumor; introducing a self-attention mechanism to enhance the attention capability of the model on the tumor area and the accuracy of feature extraction; performing image segmentation through the improved model based on the U-Net architecture; according to the method, the advantages of a CNN and a VisionTransform are combined, local and global features are extracted by using a hybrid architecture, and position codes are added into the Transform so as to enhance the perception capability of spatial position information; adopting a comparative learning strategy to improve the discrimination capability of the model by comparing positive and negative sample pairs, and performing multi-task learning in combination with a tumor classification task and a segmentation task; designing a combination of a weighted cross entropy loss function and a Dice loss function, and improving the sensitivity to a small tumor region; and performing post-processing by using a conditional random field (CRF) to optimize a segmentation boundary, and eliminating small noise points and filling holes of a segmentation region by applying morphological operation to further optimize a segmentation result.

The invention discloses a cerebrovascular segmentation method, system and device based on a TOF-MRA image and a storage medium, and relates to the field of artificial intelligence, the method comprises the following steps: collecting TOF-MRA image data, the TOF-MRA image data comprising a cerebrovascular image and a label; preprocessing the TOF-MRA image data; constructing an image segmentation network; training an image segmentation network by using the preprocessed TOF-MRA image data; storing the optimal image segmentation network; and predicting a cerebrovascular segmentation result by using the optimal image segmentation network. According to the method, the defects of poor segmentation performance, low model training reasoning speed and the like caused by a complex topological structure of the brain blood vessel are overcome, and the accuracy of brain blood vessel segmentation and the integrity of a blood vessel map are ensured and improved.

The invention relates to a polyp image segmentation method based on pyramid vision Transform, and belongs to the technical field of digital medical treatment. The method comprises the following steps: capturing global context information through a pyramid vision Transform module, and obtaining feature maps with different resolutions from a plurality of extraction stages; detail information is extracted from the shallow feature map of the first stage through a cyclic cross attention module, and shallow features are obtained; a multi-scale feature fusion sensing module is used for carrying out fusion processing on the feature maps extracted in multiple stages, deep features are extracted, and the deep features and shallow features are fused and then used for image segmentation; and designing weighted binary cross entropy loss and weighted IoU loss to train the above process, and segmenting the polyp image after training is completed. According to the invention, through the pyramid vision Transform module, the cyclic cross attention module and the multi-scale feature fusion perception module, the global information learning ability, the detail information capturing ability and the segmentation performance are significantly improved.

The invention discloses a point cloud segmentation method and system based on two channels, and relates to the technical field of computer vision and artificial intelligence, and the method comprises the steps: collecting point cloud data; creating a feature channel according to the three-dimensional point cloud data; and performing feature fusion and classification according to the feature channel. According to the point cloud segmentation method based on the two channels, different types of features are processed in different channels, high calculation efficiency is kept while global feature dependence is captured, and the method is suitable for processing and segmentation tasks of large-scale point cloud data. Two different network architectures are used, the structure and parameters of the model are flexibly adjusted to adapt to different application requirements, and a more efficient and reliable solution is provided for a point cloud segmentation task in a complex three-dimensional scene. The method has better effects in the aspects of segmentation precision, segmentation result accuracy and universality.

The invention discloses a typical ground object target segmentation method based on multi-source data attention feature fusion, and the method comprises the following steps: (1), obtaining a data set containing a high-resolution optical image, an SAR image and a corresponding semantic tag image, and carrying out the preprocessing of the data set; (2) constructing a typical ground object target segmentation model based on multi-source data attention feature fusion, and realizing semantic segmentation of a typical ground object target; and (3) taking the preprocessed data set obtained in the step (1) as the input of the model constructed in the step (2), training the model based on a weighted cross entropy loss function, and realizing typical ground object target segmentation based on multi-source data. The invention provides an adaptive fusion algorithm based on an attention mechanism by comprehensively considering the observation advantages of SAR and optical data for different ground object targets, and better combines the respective advantage characteristics of an optical image and an SAR image, thereby achieving a better semantic segmentation effect in the field of ground object classification.

The invention provides a sea wave height detection method, medium and system based on semantic segmentation, and belongs to the technical field of sea wave height detection.The sea wave height detection method comprises the steps that a near-shore camera obtains multiple pieces of sea wave image data, and a training data set is formed after preprocessing; a GS-SegFormer semantic segmentation model is utilized, and the model adopts an improved self-attention mechanism, a Ghost module and a SimAM attention mechanism to segment a sea wave image to be detected to obtain a pixel level boundary of sea waves. Gaussian filtering smoothing processing is carried out on the segmented image, noise is reduced, pixel information is converted into an actual distance according to camera parameters, and finally the actual height of each sea wave pixel is calculated. According to the method, sea wave features are identified through a deep learning model, and the actual height is calculated by combining camera parameters. The invention solves the technical problem that the precision of subsequent height calculation is influenced because the real contour of the sea wave cannot be accurately segmented by the existing sea wave height detection method.

The invention belongs to the technical field of medical image processing, and particularly relates to a double-encoder semi-supervised heart MRI double-ventricle segmentation method based on improved SAM. A teacher model and a student model which are used both comprise double encoders, prompt encoders and mask decoders, each double encoder comprises a ViT branch and a CNN-Mama branch which are parallel, parameter fine tuning of the ViT branch is realized through a position adapter and a feature adapter, the CNN-Mama branch mainly comprises a convolution fusion Mama block and a second-order dimension attention block, and the CNN-Mama branch is used for realizing fine tuning of parameters of the CNN-Mama block. The convolution fusion Mama block introduces an enhanced convolution block on the basis of a VSS block, and the second-order dimension attention block uses a covariance matrix to mine second-order statistical information of data; carrying out weighted summation on the output features of the ViT branch and the CNN-Mama branch to obtain the output features of the double encoders; the output features of the double-encoder and the prompt encoder are input into a mask decoder to be decoded, and a segmentation result is obtained; and performing joint training on the teacher model and the student model based on interpolation consistency regularization semi-supervision, and using the trained student model as a segmentation model for double-ventricle segmentation of heart MRI. The segmentation model has strong feature extraction capability, can effectively extract the edge information of the ventricles and the boundary information of the double-ventricle connection region, and improves the segmentation precision.

The invention discloses an unsupervised domain adaptive medical image segmentation method and device based on multi-scale features, and relates to the technical field of computers, and the method comprises the steps: constructing a multi-scale feature fusion segmentation network based on double attention; taking the multi-scale feature fusion segmentation network as a generator, and constructing a first discriminator and a second discriminator; training based on the generator, the first discriminator and the second discriminator to obtain a trained multi-scale feature fusion segmentation network; and segmenting a medical image to be processed by using the trained multi-scale feature fusion segmentation network. According to the method, multi-scale features are introduced, feature representation of different levels is considered, and alignment of source domain and target domain features is realized through an adversarial feature alignment method and adversarial learning, so that the performance and generalization ability of a segmentation model on a target domain are improved; moreover, the method does not need to use a target domain label, and can well solve a problem of unsupervised domain adaptation of a medical image segmentation task.

The invention discloses a medicine image detection method based on an improved Canny operator, and the method comprises the steps: 1) collecting a medicine image, and carrying out the graying processing of the medicine image; 2) an improved bilateral filter is adopted to filter the medicine image after graying processing, and noise is suppressed; 3) carrying out gradient amplitude and phase extraction on the medicine image by adopting a four-direction Soble operator; 4) performing non-maximum suppression on the extracted gradient amplitude and phase by adopting improved non-maximum suppression, and accurately positioning a target edge; 5) performing adaptive threshold extraction by using Otsu improved based on gradient edge information to obtain a drug image edge contour; and 6) outputting the edge contour of the medicine image. By adjusting the weight value of the edge pixel point identified by the bilateral filter to be zero, the interference to the central pixel point is effectively reduced, so that the target edge and detail features of the image are reserved to the greatest extent.

The invention discloses a multi-modal data fusion perception method and system based on space-time correlation, and the method comprises the steps: firstly enabling image data to enter an image denoising module, and inputting the image data into a feature extraction module after the image data is denoised; liDAR point cloud data is calibrated to generate a depth map, and the depth map enters a feature extraction module of a LiDAR branch. The extracted image features and the extracted LiDAR features are subjected to multi-modal feature fusion and matching in a feature fusion matching module. Through a query initialization module, the system generates a group of query points in an aerial view space, carries spatial position information, and processes fused features through an adaptive attention mechanism to ensure accurate detection of targets of different scales. The fused multi-modal features are sampled and aligned in time sequence through a space-time sampling and mixing module, and finally high-precision input is provided for detection and segmentation. According to the method, the multi-modal data and the space-time association strategy are combined, so that the 3D target detection and segmentation precision in a complex scene is remarkably improved.

The invention discloses a credible medical image segmentation method and equipment based on evidence uncertainty progressive guidance. The method comprises the following steps: S1, obtaining a training sample and a corresponding label; s2, training a credible medical image segmentation model according to the training samples and the corresponding labels; s3, acquiring a test sample; s4, inputting a test sample into the trained credible medical image segmentation model for prediction, calculating parameters of Dirichlet distribution, and calculating an expected tensor of the Dirichlet distribution; s5, calculating a segmentation evidence uncertainty matrix; s6, enabling the category with the maximum category probability to be a prediction category, and enabling a prediction segmentation result output by the credible medical image segmentation model based on the test sample to be predictioni = argmax pi; and S7, the prediction segmentation result of the test sample and the segmentation evidence uncertainty matrix are visualized. According to the method, the segmentation precision is improved, the calculation cost is greatly reduced, and more accurate uncertainty estimation is provided.

The invention relates to the technical field of computer vision, and discloses a text interactive minimally invasive surgery instrument segmentation method and system, and the method comprises the steps: generating the text prompt information of an instrument, and carrying out the feature extraction of a surgery image and the text prompt information based on a vision-language large model; the visual features are strengthened and refined according to the text features, and mask probability graphs corresponding to various instruments are generated; performing weighted fusion on a plurality of mask probability graphs corresponding to different text prompt information of the same instrument category; a difficult segmentation area is obtained by analyzing the corresponding relation between the fusion mask probability graph and a preset label, the difficult segmentation area in the operation image is masked and then input into a preset image encoder, and a corresponding decoder is added to execute image reconstruction. According to the method, flexible and accurate mask segmentation prediction can be carried out on different surgical instruments on the input surgical image, and the limitation that an existing method is low in segmentation precision, poor in generalization ability and fixed in prediction category is broken through.

The invention discloses a microcirculation blood vessel automatic segmentation and quantification method based on enhanced feature fusion. The method comprises the following steps: step 1, data preparation and preprocessing; 2, designing an enhanced feature fusion network model; step 3, automatically segmenting microcirculation blood vessels; and 4, quantifying the diameter of the blood vessel based on the segmentation result. According to the enhanced feature fusion network model provided by the invention, accurate segmentation of microvessels is realized through fusion and enhancement of multi-scale features. The model shows good generalization ability on diversified clinical image data through effective data enhancement, and can adapt to image data with different illumination conditions, contrast ratios and resolutions. Therefore, the model has high practicability and reliability in practical application. According to the method, the diameter of the blood vessel is quantified through the segmentation result, the diameter of the microvessel can be accurately measured, and powerful support is provided for evaluation of the clinical microcirculation function.

The invention belongs to the field of mass spectrum imaging, and particularly provides an image segmentation method for a mass spectrum imaging image of an ESI source, and the method comprises the steps: (S1) dividing the mass spectrum imaging image into a plurality of mass spectrum imaging channels according to a mass-to-charge ratio; (S2) selecting a mass spectrum imaging channel where target ions are located, and preprocessing each pixel; (S3) carrying out preliminary segmentation on the spectrogram data; (S4) performing interval division on the mass spectrum data of the channel where the target ions of each pixel are located, calculating the lower area of each interval, selecting the central mass-to-charge ratio of each interval as the abscissa of the intensity value of each interval, and setting the intensity value of the interval with the ratio of the central mass-to-charge ratio to the highest intensity value lower than a set value as 0; (S5) analyzing, training and optimizing the transformed mass spectrum data; and (S6) the optimal combination model obtained in the step (S5) is used for classification of other pixel points of the mass spectrum imaging image, and an image segmentation result is obtained. The method has the advantages of accurate segmentation and the like, and is applied to mass spectrum imaging of the ESI source.

The invention discloses a rock pore three-dimensional reconstruction method and system based on an adaptive hierarchical cube algorithm, and a storage medium, relates to the technical field of rock structure measurement, and aims to solve the problems of low extraction execution efficiency, ambiguity and poor reconstruction precision of an existing moving cube algorithm. According to an image segmentation model based on RefineNet, the model adopts three 3 * 3 convolution cascading to replace 7 * 7 convolution, and a coordinate attention mechanism is introduced behind each block of an encoder to improve the segmentation precision and efficiency of a key area; dividing the cube into tetrahedrons by adopting a saddle point-adaptive layered downward subdivision method, solving intersection point coordinates and normal vectors, and constructing a triangular patch index; determining the contour surface shape penetrating through the cube by adopting a cube lookup table, and merging triangles of all tetrahedrons to form a complete contour surface; and creating a new triangular patch by using the vertex of the edge of the hole and the newly generated vertex, and repairing the hole to obtain the accurately reconstructed three-dimensional hole of the rock.

The embodiment of the invention discloses an MRI (Magnetic Resonance Imaging) image segmentation model training method, an MRI image segmentation method and related devices. The encoder of the U-Net network performs down-sampling on the multi-modal MRI image to obtain a feature map of each modal MRI image, the cross-modal module fuses the plurality of feature maps of the multi-modal MRI image, the decoder of the U-Net network performs up-sampling on a fused feature matrix, the up-sampled feature matrix is mapped into a target feature map, and the target feature map is mapped into the multi-modal MRI image. And processing the target feature map to obtain a prediction segmentation result, and adjusting model parameters according to the prediction segmentation result and the labels to obtain a target segmentation model. The cross-modal module has the capability of processing the multi-modal MRI image, can capture the long-distance dependency relationship of the multi-modal MRI image input, and greatly improves the segmentation precision of the brain tumor region of the MRI image. Lightweight convolution operation is adopted in up-sampling and down-sampling of the U-Net network, and the risk of overfitting is effectively reduced on the premise that the model structure is not complicated and the parameter quantity is not increased.

The invention provides a point cloud semantic segmentation method for improving a RandLA-Net network. The method comprises the following steps: step 1, carrying out data preprocessing grid downsampling; step 2, calculating a point cloud normal vector by a local curved surface fitting method; thirdly, local multi-dimensional feature fusion is carried out, and local geometric information is captured; fourthly, important features are automatically learned and aggregated through a self-attention module and attention pooling; 5, cascading an up-sampling result with the intermediate feature of the coding layer through jump connection; and 6, predicting the semantic tag by using a full connection layer. According to the invention, high-precision and high-efficiency automatic point cloud semantic segmentation in a large-scale outdoor scene is realized.

The invention discloses a blood vessel segmentation method and device based on large model fine tuning, equipment and a medium, and the method comprises the steps: obtaining a blood vessel image, carrying out the preprocessing of the obtained blood vessel image, and obtaining a training set; performing fine tuning on a pre-trained image segmentation model by adopting the training set, so that the image segmentation model learns related features of blood vessel segmentation; wherein the low-rank adaptive fine tuning technology is adopted to perform fine tuning on the image segmentation model: two parallel low-rank matrixes are inserted into a Transform layer in the image encoder, the input is processed by the two low-rank matrixes in sequence, and the processing result and the original output are added to obtain the final output. According to the method, the defects of a large model in a blood vessel segmentation task are overcome through efficient parameter fine tuning and a multi-scale feature fusion module. Accurate blood vessel segmentation can be realized under limited annotation data, the difficulty and cost of medical image segmentation tasks are greatly reduced, and the method can be widely applied to the technical field of medical image processing.

The invention relates to the technical field of artificial intelligence and automatic driving, in particular to a road semantic segmentation method based on a lightweight multi-scale fusion Transform network, which comprises a lightweight encoder network module, a lightweight multi-scale fusion Transform network module, a lightweight decoder network module and a space attention network module. The light-weight encoder network module mixes depth separable convolution and asymmetric dilated convolution, not only effectively expands a receptive field to capture rich context information in a road scene, but also significantly reduces calculation complexity, and the light-weight multi-scale fusion Transform network module reconstructs feature maps extracted from multiple scales, so that the reconstruction efficiency is improved. The module uses a separable cross-channel attention mechanism to effectively capture internal dependence and association between feature maps of different scales. The road scene semantic segmentation method designed by the invention is high in accuracy, low in complexity, good in real-time performance and suitable for vehicle-mounted equipment, realizes good balance between segmentation precision and calculation efficiency, and has important application value in the field of automatic driving.

A heart ultrasound image segmentation method based on a state space model and feature interactive perception relates to the field of image segmentation, and a dual perception state space enhancement module DPSSEM uses two SSM modules and combines the two SSM modules with SAM and CAM respectively, so that more information can be obtained from two dimensions of space and channel, and the image segmentation efficiency is improved. The dual perception state space enhancement module DPSSEM carries out fine interaction and fusion among different feature maps, the feature identification capability and expression capability are significantly enhanced, the accuracy and robustness of a heart structure in ultrasound image segmentation are effectively improved, and the feature interaction perception decoding module FIPDM carries out multi-feature processing branch and interaction operation, so that the accuracy and robustness of the heart structure in ultrasound image segmentation are effectively improved. And performing multi-path refinement and enhancement on the extracted features in a decoding stage.

The invention discloses a multi-modal brain tumor image segmentation system based on a sparse attention mechanism, and relates to the technical field of medical image processing, and the system comprises a data obtaining unit which is used for obtaining multi-modal brain tumor data and carrying out the preprocessing of the multi-modal brain tumor data to obtain multi-modal data; the feature extraction unit is used for extracting a first specific feature Fm of each mode in the multi-mode data based on a first encoder; the feature fusion unit is used for obtaining a first feature f1 based on the first specific feature Fm and a first fusion token Ff; based on a modal sparse mask fusion Transform model, interacting the first feature f1 to obtain a second feature Z, based on the second feature Z, extracting a weight IMm of each modal, based on the weight IMm, obtaining a third feature # imgabs0 #, based on spatial dimensions, and based on the third feature # imgabs0 #, obtaining a second feature Z; the prediction unit is used for inputting the fourth feature # imgabs3 # into a brain tumor segmentation model to obtain a first loss L1, obtaining a prediction segmentation result based on the first loss L1, and obtaining a prediction segmentation result based on the prediction segmentation result; the problems that an existing segmentation mode based on shared feature representation is insensitive to brain tumor information, information redundancy occurs in brain tumor feature fusion when modals are lost, and important brain tumor information is ignored can be solved.