86 results about "3d segmentation" patented technology

The invention discloses an object three-dimensional reconstruction method, device and system based on deep learning. The reconstruction method comprises the following steps: acquiring a multi-view color image of an object through a controllable image acquisition device; reconstructing a sparse three-dimensional point cloud by using a motion recovery structure method and obtaining a camera pose; initializing parameters of the three-dimensional Gaussian sputtering model based on the sparse point cloud and performing training optimization; a target object semantic segmentation data set is constructed, and a low-rank adaptive technology is adopted to finely segment all models; generating prompts through an open vocabulary detection model at each view angle, obtaining an accurate segmentation mask, and optimizing a three-dimensional segmentation weight by adopting a joint loss function fusing color consistency loss and edge perception loss; and finally outputting the color three-dimensional point cloud of the target object. According to the method, the original image is segmented, so that the influence of the quality of the rendered image is avoided; the segmentation precision of the model in a specific scene is improved through field adaptive fine tuning; and the accuracy of the segmentation boundary is ensured by adopting a double-loss joint optimization mechanism.

The invention discloses a multi-axis RWKV-UNet + + multi-mode MRI (Magnetic Resonance Imaging) brain tumor segmentation method, and belongs to the technical field of medical image processing. According to the invention, multi-modal MRI three-dimensional body data is input and preprocessed, and fusion features are output through a modal fusion module; the fusion features are input into an encoder containing multi-axis RWKV sequence modeling, and long-range dependence is extracted; after the output of the encoder is processed by the bottleneck layer, the global Token aggregator converges the global context and reinjects the global context; the enhanced features are input into a UNet + + nested topology decoder, the jump features are fused with the up-sampling features after being subjected to jump RWKV semantic alignment, and finally a three-dimensional segmentation probability graph is generated through mapping. The method is mainly used for accurate three-dimensional segmentation of the multi-mode MRI brain tumor, and provides support for clinical brain tumor diagnosis and treatment.

The invention discloses a brain tumor MRI image segmentation method based on a soft clustering KAN network, and belongs to the technical field of medical information processing, and the method comprises the steps: carrying out the voxel alignment and intensity normalization of a multi-modal three-dimensional brain tumor MRI image, and constructing a unified input sample; layer-by-layer downsampling and multi-scale feature extraction are realized through a KAN residual encoder formed by alternately cascading double-layer KAN attention subnets and three-dimensional maximum pooling layers; three-dimensional position coding is added to the highest-layer features, and bottleneck features containing global structure priori are obtained through soft K-means clustering bottleneck layer modeling; fusing the jump connection feature and the up-sampling feature by using an attention gating mechanism to complete decoding; and through soft clustering regularization and a multi-scale depth supervision constraint optimization model, finally through fusion of a main segmentation branch and a refined branch, outputting a multi-subarea three-dimensional segmentation result of the whole tumor, the tumor core and the enhanced tumor.

This invention discloses an adaptive edge-aware 3D medical image segmentation method, with the following specific steps: S1, constructing an adaptive edge-aware network, which includes an encoder and a decoder, with a skip connection between the encoder and decoder; S2, acquiring and processing a 3D medical image; S3, inputting the preprocessed image from step S2 into the encoder of the adaptive edge-aware network through a patch partitioning layer, then into the decoder through residual blocks and adaptive weight matching blocks. The decoder output and the original input image are skip-connected through adaptive weight matching blocks, and finally, the image segmentation result is output through residual blocks and Fourier convolution. This invention exhibits stronger robustness and boundary accuracy in multi-organ 3D segmentation tasks, providing an efficient and scalable solution for medical image segmentation.

The invention discloses a citrus X-ray image rapid reconstruction and defect segmentation method based on sparse point cloud and 3DGS, and relates to image processing, and the method comprises the following steps: S1, obtaining X-ray image data in a citrus under a sparse view angle; s2, performing three-dimensional reconstruction on the X-ray image data by adopting a three-dimensional Gaussian point cloud reconstruction method based on adaptive density control to obtain a reconstructed three-dimensional body; s3, performing automatic defect segmentation on the reconstructed three-dimensional body through the three-dimensional segmentation model to obtain two-dimensional defect mask slices and three-dimensional defect voxel data; and S4, according to the two-dimensional defect mask slices and the three-dimensional defect voxel data, performing comprehensive evaluation on the internal defects of the citrus, and outputting an internal quality report of the citrus. According to the method, full-process automatic processing from sparse view angle X-ray image data acquisition to three-dimensional volume reconstruction to automatic defect segmentation and type identification is realized, and the purpose of efficiently, accurately and losslessly identifying the internal defects of the citrus is achieved.

The invention relates to an airway path planning method and device based on a throat CT image and computer equipment. The method comprises the following steps: acquiring a throat CT image; the throat CT image is preprocessed, the preprocessing includes unifying thickness parameters of the throat CT image, and data expansion processing is carried out on a target layer, including the glottis part, in the throat CT image with the consistent thickness parameters in a copying mode; inputting the preprocessed throat CT image into the trained three-dimensional segmentation model for three-dimensional segmentation to obtain a segmented airway three-dimensional structure; obtaining a path constraint condition according to the obstacle differentiation weights of different areas in the divided airway three-dimensional structure; layering treatment is carried out on the divided airway three-dimensional structure, and a multi-layer airway cross section is obtained; obtaining a center point sequence based on the center point coordinates of the cross sections of the multiple layers of airways; and obtaining an initial airway path corresponding to the throat CT image based on the target point in the airway, the center point sequence and the path constraint condition.

The application discloses a double-branch Raio gating graph aggregation multi-modal meningioma segmentation method, belongs to the cross technical field of computer vision and medical image processing, is used for meningioma segmentation, and comprises the following steps: preparing a data set, constructing a deep neural network model, and performing neural network training; inputting the data set into the trained deep neural network model; first inputting a double-branch multi-modal input module to output a multi-modal fusion feature tensor; then inputting the multi-modal fusion feature tensor into a hierarchical Swin Transformer encoder to output three-dimensional segmentation masks of enhanced tumors, tumor cores and whole tumor regions of meningioma. Through double-branch multi-modal input fusion, hierarchical Swin Transformer coding, edge perception modulation and Laplace gating graph aggregation, high-precision three-dimensional segmentation of enhanced tumors, tumor cores and whole tumor regions of meningioma is realized.

This invention discloses a method for esophageal cancer tumor target region segmentation based on cross-modal feature fusion of PET / CT images. The method uses a Transformer-fused Attention Progressive Semantic Nested Network (TransAttPSNN) as the 3D segmentation model for esophageal cancer tumor target regions. The TransAttPSNN network has an AttPSNN backbone structure and includes two segmentation networks: one for PET streams and the other for CT streams. Transformer cross-modal adaptive feature fusion modules are embedded in the different feature levels of the two segmentation networks. Compared with existing technologies, this invention effectively improves the segmentation accuracy of esophageal cancer tumor target regions and achieves better segmentation performance.

The application provides a multi-modal tissue segmentation method and a surgical navigation system, and the method comprises the following steps: inputting three-dimensional medical images of multiple modes into corresponding deep learning network models respectively for segmentation to obtain corresponding three-dimensional segmentation results; registering the three-dimensional medical images of each mode to obtain a registration relationship; and according to the registration relationship, combining the three-dimensional medical images of each mode to weight and fuse the three-dimensional segmentation results of each mode according to preset weights of each type of tissue. The application trains a segmentation model for each mode of medical image, each deep learning model has stronger segmentation capability for the medical image of the corresponding mode, and has higher segmentation precision; different types of medical images are respectively designed with preset weights (mode weights) of each type of tissue, and the segmentation results are fused according to the mode weights in the fusion process, so that the tissue structure characteristics are more fully reflected, and the segmentation precision is improved.

An ultrasound imaging system (110) may include a transducer configured to transmit and receive an ultrasound signal; a matching layer configured to have an acoustic impedance between the tissue to be imaged and the material of the transducer; a damping block configured to absorb ultrasonic energy; and a processing circuit (316). A processing circuit (316) may acquire medical imaging data of an anatomical feature of a subject. A processing circuit (316) may determine a position of an anatomical structure relative to an anatomical feature of a subject. A processing circuit (316) may generate a four-dimensional (4D) model of an anatomical feature of a subject, the four-dimensional (4D) model including a visual indicator identifying a location of an anatomical structure relative to the anatomical feature of the subject. The processing circuitry (316) may display the 4D model via the user interface.

The invention relates to the technical field of artificial intelligence, and provides a living chicken organ character determination method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining a CT image of a living chicken, inputting the CT image into a three-dimensional segmentation model, and outputting a segmentation result of a target organ; wherein the three-dimensional segmentation model is used for performing multi-stage feature extraction on the CT image, calculating a cross-stage dependency relationship among coding features of the first N-1 stages, then performing feature fusion on high-level features and low-level features in a coding feature sequence formed by the stages step by step, and finally generating a segmentation result based on the decoding feature of the first stage; a character measurement result is determined on the basis of the segmentation result. According to the living chicken organ character determination method provided by the invention, through combination of CT imaging and the three-dimensional segmentation model, automatic determination of organ characters is realized, determination efficiency and accuracy are greatly improved, and a reliable genetic evaluation basis is provided for poultry breeding, so that a good variety breeding process is significantly accelerated.

A three-dimensional medical image segmentation system and method based on three-dimensional spatial enhancement is disclosed, belonging to the field of medical image processing technology. It solves the technical problem of accurately depicting the three-dimensional morphological features of organs when the RWKV architecture is directly applied to three-dimensional medical image segmentation tasks. The system includes an encoder and a decoder. The encoder includes several downsampling layers containing three-dimensional spatial enhancement modules, and the decoder includes the same number of upsampling layers containing three-dimensional spatial enhancement modules as the encoder. The three-dimensional spatial enhancement modules in the encoder are connected to the corresponding three-dimensional spatial enhancement modules in the decoder via skip connections. The three-dimensional spatial enhancement modules are improved from existing RWKV modules, with improvements including: changing the RWKV module's serialization modeling operation from a single sequence direction to three sequence directions; and adding spatial shift operations at the data input ends of the two hybrid modules of the RWKV module.

The invention provides a three-dimensional shape quantification method and device for open field vegetables, and relates to the field of crop growth monitoring, and the method comprises the steps: collecting depth images and RGB images of a current vegetable at a plurality of different visual angles; obtaining an initial point cloud of the current vegetable based on the depth image and the RGB image; inputting the initial point cloud into a three-dimensional segmentation model to obtain a segmentation result output by the three-dimensional segmentation model; obtaining a target point cloud of the current vegetable based on the segmentation result; and based on the target point cloud, determining a quantization parameter of the current vegetable. According to the three-dimensional shape quantification method for the open field vegetables, comprehensive and accurate quantitative characterization of the three-dimensional shapes of the head vegetables growing in the open field environment is achieved, and reliable data support can be provided for precise agricultural management.

The invention discloses an image segmentation method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining a to-be-processed original 3D image which comprises a plurality of 2D slice images; first prompt information is received, second prompt information is generated based on the first prompt information, and the first prompt information indicates a target object in the 2D slice image; and based on the second prompt information, performing segmentation processing on the original 3D image through the 3D medical basic large model to obtain a 3D segmentation result of the target object. According to the image segmentation method, a complete 3D segmentation result can be obtained only by giving a small amount of first prompt information on the 2D slice image, a user does not need to prompt each layer of image, the workload is reduced, a medical basic large model is adopted, the applicability is wider, and end-to-end training does not need to be carried out.

This disclosure relates to a vascular image processing method, apparatus, readable storage medium, and electronic device. The method includes: acquiring a CTA image of a blood vessel; obtaining a three-dimensional segmentation result of the blood vessel based on the CTA image; extracting the centerline of the blood vessel based on the three-dimensional segmentation result; generating a local coordinate system for each point on the centerline based on a Rotation Minimizing Frame (RMF); and obtaining a three-dimensional straightened image of the blood vessel based on the local coordinate system. The three-dimensional straightened image of the blood vessel generated based on the local coordinate system of the RMF can clearly and intuitively present the morphology of the blood vessel, including the main artery, branches, and surrounding tissues. Different angles can be rotated around the central axis of the three-dimensional straightened image to obtain a two-dimensional straightened image at the corresponding angle, thereby achieving 360-degree comprehensive qualitative and quantitative analysis of the blood vessel, facilitating doctors to more quickly and accurately locate lesions.

The invention discloses a medical image multi-target segmentation method, system and device based on convolution and Mama frameworks and a medium, and belongs to the technical field of medical image recognition, and the method comprises the steps: obtaining an original three-dimensional medical image, and carrying out the preprocessing of the original three-dimensional medical image, and generating a standardized input tensor; performing feature extraction to generate a coding feature sequence; carrying out long-range dependence modeling and applying a MonteCarlo attention mechanism according to the coding feature sequence, and generating a bottleneck feature and a jump connection feature of global context enhancement; splicing the bottleneck feature and the jump connection feature to generate a segmented feature map; mapping the segmentation feature map into a three-dimensional segmentation mask, and performing post-processing and spatial restoration to generate a three-dimensional multi-target segmentation result; according to the invention, through constructing a three-dimensional segmentation architecture fusing convolution local modeling and Mama state space long-range modeling, the defects of small target segmentation, cross-layer dependence modeling and reasoning efficiency in the prior art are overcome.

This invention discloses a medical image registration method based on frozen pre-trained segmentation encoder transfer adaptation. Its key feature is the use of a shared frozen 3D segmentation encoder as the feature backbone network. Multi-scale anatomical perceptual feature pyramids are extracted from the image to be registered and the target image. During the coarse-to-fine residual pyramid decoding process, feature space similarity constraints and a difference-product interaction fusion module are combined to predict deformation updates step-by-step and generate the final deformation field, thereby achieving accurate registration of the image to be registered to the target image. Compared with existing technologies, this invention utilizes anatomical prior information to improve the accuracy, deformation rationality, and cross-dataset generalization ability of medical image registration. It can adapt to different 3D pre-trained segmentation encoders and coarse-to-fine registration decoding architectures, improving the model's performance in medical image registration tasks. It also solves problems such as strong dependence on intensity similarity, insufficient robustness under limited data conditions, and weak generalization ability under domain offset conditions.

The invention relates to the field of medical image analysis, in particular to a brain metastasis tumor tiny focus screening system based on medical image processing. Comprising a medical image data acquisition module, an image preprocessing module, a tiny focus enhancement processing module, a tiny focus candidate area generation module, a tiny focus fine segmentation module, a focus intelligent identification module and a screening result output module. A boundary sensitive constraint mechanism is introduced, a higher segmentation weight is given to a lesion boundary voxel, the lesion boundary and an internal region are finely distinguished, and the positioning precision and the three-dimensional segmentation accuracy of the tiny lesion boundary are improved; according to the method, the multi-scale lightweight coding network is adopted on the three-dimensional volume data block to extract the lesion features, the space-channel mixed attention mechanism is combined to dynamically enhance the tiny lesion features, background interference is inhibited, and the reliability and screening precision of tiny lesion recognition are improved.

The application belongs to the field of intelligent medical treatment, and particularly relates to a bladder tumor image segmentation method, device, medium and program product. The method comprises the following steps: S101, acquiring an image of a bladder tumor patient; S102, inputting the image into a 2D U-Net for tumor segmentation to obtain a 2D segmentation result; S103, based on the 2D segmentation result, traversing the shape of the tumor, if the current tumor shape is regular, inputting the image into a 3D U-Net for tumor segmentation to obtain a 3D segmentation result, and taking the segmentation result of the current tumor in the 3D segmentation result; if the tumor shape is irregular, taking the segmentation result of the current tumor in the 2D segmentation result, and after the traversal is completed, merging and outputting the segmentation results of multiple tumors. The application can optimize the segmentation performance by using the complementary advantages of the 2D-3D method, and obtain better segmentation results.

The present application relates to the technical field of medical image processing and computer-aided diagnosis, and particularly relates to a multi-modal magnetic resonance image segmentation method based on MSBA-Net, aiming at significantly reducing the model parameter quantity and improving the training and inference efficiency under the premise of ensuring the segmentation accuracy. The method comprises the following steps: acquiring multi-modal brain tumor magnetic resonance images and preprocessing to obtain preprocessed images; inputting the preprocessed images into a compact 3D segmentation network MSBA-Net to extract multi-scale deep semantic features; based on the multi-scale deep semantic features, outputting a preliminary segmentation prediction map through a decoder, and outputting an auxiliary segmentation prediction map at four resolution levels by using a deep supervision mechanism; constructing a boundary-guided hybrid loss function, training the compact 3D segmentation network MSBA-Net by using the boundary-guided hybrid loss function, and segmenting the test images in the test stage to output the segmentation masks of the whole tumor region, the tumor core region and the enhanced tumor region of the brain tumor.

The application provides a three-dimensional segmentation method for age-related macular degeneration (AMD) high-reflection area of human fundus, an electronic device and a storage medium, and the method comprises the following steps: obtaining an initial fundus optical coherence tomography image output by an optical coherence tomography system; a deep learning network for segmenting the AMD high-reflection area of the fundus is pre-trained, the network can automatically lock the segmentation area and extract feature information of different sizes; the initial fundus optical coherence tomography image is input into the deep learning network model for segmenting the AMD high-reflection area of the fundus, which is obtained by pre-training, and the initial fundus optical coherence tomography image is three-dimensionally segmented by the deep learning network model to obtain a three-dimensional stereoscopic lesion area image. The deep learning model can correctly, intuitively and clearly segment the AMD high-reflection area of the fundus of a patient, thus providing great convenience for clinical diagnosis and follow-up of AMD, and improving the acceptance and recognition of the patient to the diagnosis result.

The invention relates to a cerebrovascular CTA segmentation method and system based on a three-dimensional convolutional network. The method comprises the following steps: acquiring a two-dimensional slice file of a cerebral vessel; the two-dimensional slice files are stacked according to the spatial position relation, and three-dimensional CTA volume data are obtained; performing contrast enhancement on the three-dimensional CTA volume data based on a preset window width and a window level value to obtain three-dimensional image volume data in a target range; inputting the three-dimensional image volume data into a preset three-dimensional convolutional neural network to obtain a three-dimensional probability graph; comparing the probability value of each voxel in the three-dimensional probability graph with a global probability threshold to obtain a binary three-dimensional segmentation mask; and performing morphological optimization on the binary three-dimensional segmentation mask to obtain a three-dimensional blood vessel segmentation mask. By adopting the method, a complex blood vessel structure from a millimeter-level trunk to a submillimeter-level tip can be accurately captured, and the segmentation precision of cerebrovascular CTA is improved.

The invention relates to the technical field of medical image processing, and particularly discloses a coronary CTA stenosis degree intelligent evaluation method based on reinforcement learning, which comprises the following steps: firstly, carrying out preprocessing and blood vessel segmentation on a coronary CTA three-dimensional image to obtain three-dimensional segmentation data containing blood vessel probability distribution; then, constructing a blood vessel tree topological graph based on the segmentation data, and reasoning, identifying and correcting connection errors through a topological relation to obtain an optimized topological structure; iteratively optimizing the segmented data by using the optimized topological structure to obtain a three-dimensional blood vessel structure with correct topology, and calculating a connection matrix and a geometric attribute matrix of a blood vessel tree; and finally, inputting the matrix data into a stenosis evaluation model constructed based on a graph attention network and a recurrent neural network, and outputting a stenosis degree evaluation result of each blood vessel segment.

This invention proposes an ISAR satellite attitude and size estimation method based on component 3D segmentation to address the poor robustness of existing satellite attitude and size estimation techniques. The implementation steps are as follows: 1) Calculate the range and azimuth resolution of the ISAR image of the satellite target; 2) Perform 3D reconstruction of the satellite target; 3) Perform 3D segmentation of the satellite target components; 4) Obtain the estimated satellite attitude and size results from the ISAR image. This invention obtains the 3D point set of the satellite target body and the 3D point set of the solar panel through 3D reconstruction and component 3D segmentation, and then estimates the true attitude and size of the satellite target from these sets. The entire process does not require solving non-convex optimization functions, thus ensuring the robustness of the algorithm.

This invention discloses a deep learning-based method and electronic device for lung CT image analysis, including constructing a dataset; establishing an expert pool, including vectorizing CT image data and constructing an expert pool segmentation sub-model containing multiple expert segmentation sub-models to process data queues with specific image distribution characteristics; constructing a gating feature extractor to map CT images into high-dimensional semantic vectors that reflect the confidence and distribution labels of abnormal regions; and using a gating network to adaptively segment CT images, selecting the optimal expert segmentation sub-model from the expert pool based on the input mixed image feature vector. This invention improves the segmentation accuracy for heterogeneous lesions, achieves adaptive selection for different image phenotypes, outputs a three-dimensional segmentation mask and attribute prediction vectors to support subsequent quantitative assessment and clinical decision-making, alleviates the sample imbalance problem, and can accurately route rare lesion morphologies.

This invention discloses a 2D and 3D collaborative learning method for MRI lumbar spine and intervertebral disc segmentation, belonging to the field of intelligent medical image analysis technology. The method performs anisotropic resampling, intensity normalization, and region clipping on lumbar spine MRI data, and then inputs these data into 2D and 3D segmentation branches respectively. Cross-dimensional information interaction is achieved through bidirectional mapping from slice to volume and from volume to slice. A dynamic gating mutual learning mechanism is constructed based on confidence, consistency, and axial continuity to suppress low-confidence pseudo-supervision noise. Furthermore, anatomical topological priors are introduced into the segmentation results to perform instance separation, segment labeling, and topological validity correction on the vertebral bodies and intervertebral discs. Experiments demonstrate that this method can significantly improve the segmentation accuracy of complex anatomical structures and effectively solve the boundary ambiguity problem in anisotropic data. This invention forms a 2D and 3D collaborative learning segmentation approach that is both innovative and interpretable.

The application discloses a three-dimensional medical image segmentation method fusing multi-view information, mainly comprising the following steps: (1) reconstructing two-dimensional slice images from sagittal, coronal and transverse three view directions for a three-dimensional medical image to be detected; (2) adopting a U-shaped 2D convolution network based on a hollow space pyramid convolution to segment the two-dimensional slices in different view directions; and (3) adopting a light 3D convolution network to fuse the segmentation results in different view directions to obtain accurate three-dimensional segmentation results. The application can effectively overcome the problems that a 2D network cannot extract three-dimensional space features and a 3D network has a large memory overhead, and can obtain accurate segmentation results in the case of a light network.

The present disclosure relates to a computer-implemented method of machine learning, the method comprising providing a dataset of training samples. Each training sample comprises a pair of 3D modeled object parts, which pair is labeled with a respective value. The respective value indicates whether the two parts belong to the same segment of a 3D modeled object. The method further comprises learning a neural network based on the dataset. The neural network is configured for taking as input two parts of a 3D modeled object representing a mechanical component, and for outputting a respective value. The respective value indicates a degree to which the two parts belong to the same segment of the 3D modeled object. The neural network is thereby usable for 3D segmentation. The method constitutes an improved solution for 3D segmentation.

The invention relates to a travelable region labeling method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring a target point cloud file corresponding to a target vehicle and a plurality of corresponding multi-view images; inputting the plurality of multi-view images into a feature processing module in a target labeling model to obtain respective corresponding target features of the plurality of multi-view images on the plurality of columns; respectively inputting the target features into a distance analysis module and a type analysis module in a target labeling model, and carrying out driving domain distance analysis and type analysis to obtain target distance labeling data and target type labeling data corresponding to the plurality of multi-view images on the plurality of columns; based on the target distance labeling data and the target point cloud file, determining a drivable domain three-dimensional segmentation region in the target point cloud file; and based on the three-dimensional segmentation region of the travelable region and the target type labeling data, labeling the travelable region of the target point cloud file. According to the embodiment of the invention, the accuracy of travelable region labeling can be improved.

The application discloses a TOF-MRA three-dimensional segmentation method and system based on orthogonal projection enhancement and tubular perception coding. The method comprises data preprocessing, three-dimensional tubular structure segmentation network construction, network training and output of a segmentation result. Through orthogonal projection enhancement of an input end, directional perception tubular convolution coding and small tubular structure reservation down sampling, the visibility, coding stability and structure integrity of weak response small tubular structures in three-dimensional body data are improved, so that the automatic segmentation performance of tubular targets in three-dimensional images is improved.