130 results about "3d segmentation" patented technology

The invention belongs to the technical field of three-dimensional scene understanding, and particularly relates to a training method and device for a three-dimensional open vocabulary semantic segmentation model. The training method comprises the steps of obtaining multi-view RGB-D images of a target area, performing multi-stage reasoning on each image through a visual language model, generating a target vocabulary list, prompting a two-dimensional segmentation model to establish a pixel-level text label, performing depth mapping on the images to generate a first point cloud, and generating a second point cloud; mapping the text tag to the first point cloud to generate a point-by-point text tag; pre-training a neural network model with a sparse encoder-decoder structure by taking the point-by-point text label as a supervision signal, and generating a three-dimensional segmentation model on the first point cloud; and for the second point cloud of the complete scene of the target area, matching point feature embedding and text embedding with the highest similarity in the shared vision-language feature space, generating a credible point-text tag pair, and finely adjusting the three-dimensional segmentation model based on the credible point-text tag pair.

The invention relates to the technical field of medical image processing, in particular to a lung tumor CT image 3D segmentation method and system based on multi-modal image fusion. The method comprises the following steps: acquiring a lung tumor CT image; determining a first texture feature based on the lung tumor CT image; identifying a lung tumor boundary by using the first texture feature; detecting a nodule protrusion area in the boundary of the lung tumor; obtaining tissue infiltration data from the nodule protrusion area; determining a second texture feature according to the tissue infiltration data; determining a tumor heterogeneity feature according to the first texture feature and the second texture feature; evaluating the potential malignancy degree by utilizing tumor heterogeneity characteristics; and dividing a tumor risk area of the lung tumor CT image based on the potential malignancy degree. According to the invention, accurate heterogeneity identification and risk region division of the lung tumor CT image are realized based on a medical image processing technology, and the accuracy of lung tumor 3D segmentation is improved.

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.

The invention discloses a cerebral hemorrhage heterogeneity quantitative evaluation system based on cyclic cross-attention clustering, and relates to the field of cerebral hemorrhage evaluation, and the system comprises an image segmentation module which is used for carrying out the three-dimensional segmentation of a brain plain-scan CT image through an nnU-NetV2 model, and outputting the binary masks of a hematoma region and an edema region; the feature extraction module is used for extracting high-dimensional pixel features from the mask region and generating an initial feature embedding matrix; the cyclic cross-attention clustering module is used for dynamically updating a clustering center through a multi-scale cyclic EM cross-attention mechanism and executing clustering analysis on the feature embedding matrix; the heterogeneity scoring module is used for calculating information entropy and generating hematoma and edema heterogeneity scores; and the prognosis prediction module is used for predicting the hematoma expansion risk and the neural function outcome through a machine learning model. According to the scheme, objective quantification of cerebral hemorrhage heterogeneity can be realized, and the prediction accuracy of the hematoma expansion risk and the neural function outcome is improved.

Systems and methods for segmenting 3D images are provided. In an embodiment, the system includes a neural network having a low-resolution module trained to infer a complete low-resolution segmentation from an input low-resolution 3D image and to generate corresponding low-resolution feature maps; and a high-resolution module trained to infer a complete high-resolution segmentation from an input high-resolution 3D image and the feature maps from the low-resolution module. Methods for training the neural network and measuring a volume of an object using the 3D segmentations are also described.

Segmenting method, for carrying out 3D segmentation of a sample, the sample comprising at least one biological object, the sample developing over time, such that at least one biological object divides or changes shape or position over time, the method comprising:at various times, acquiring a stack of images (P(t)) of the sample;segmenting images, such as to obtain masks corresponding to each biological object;implementing a segmentation algorithm that is said to be prompted, such as to use masks obtained, for an object, in an image, to define masks, for the same object, in another image.

This invention discloses a radiotherapy pathway planning method based on surface contour monitoring, specifically relating to the field of medical image data processing. It addresses the issues of low reusability and intelligence in existing medical radiotherapy pathway planning methods. By collecting labeled target area feature parameters and incident point distribution from historical cases, a distribution prediction model for radiotherapy incident points is constructed. Three-dimensional segmentation of the target area and organ structures is performed in the current patient's CT images, establishing a unified voxel coordinate system. Based on the prediction results, an array of incident points covering the target area surface is constructed, generating a candidate radiotherapy pathway set, and risk organ identification is performed for each pathway. Furthermore, the spatial distance between the pathway trajectory and the risk organ contour surface is combined to calculate a comprehensive risk score for the candidate pathways. Finally, dose simulation is performed on the candidate pathways according to the score order, outputting radiotherapy pathway planning results that meet dose and safety requirements, thus improving the accuracy and clinical applicability of pathway selection.

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.

The invention discloses a three-dimensional target positioning method and system based on two-dimensional segmentation and three-dimensional reconstruction and related equipment, and belongs to the technical field of three-dimensional localization, and the method comprises the steps: carrying out the key target segmentation of a visual perception video based on a two-dimensional image segmentation model, obtaining a video frame, and carrying out the RGB label labeling of the video; inputting the marked video frame into the three-dimensional reconstruction model to obtain a three-dimensional point cloud model with a mask; screening point clouds on the basis of label numerical values; analyzing the screened point clouds by using a clustering algorithm to generate a clustering point set; and extracting three-dimensional space center points of different clustering point sets as three-dimensional positions of the segmented target, and obtaining a spatial position of the target relative to the shooting point. According to the method, the robot can be better supported to complete identification, three-dimensional segmentation and positioning of a key target by using the instantly acquired two-dimensional optical image information, and the robot is supported to complete tasks such as navigation and operation by using related information.

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 application is suitable for the technical field of CT image processing, and provides a segmentation method for positive lymph nodes in a CT image based on multi-task learning, comprising: preprocessing and enhancing data of a CT image sequence, constructing a multi-task deep learning model, extracting features through an encoder, performing a classification task by using a classification module, and segmenting positive lymph nodes by using a segmentation module combined with a classification result. The application is excellent in classification accuracy (up to 91.1%) and DICE coefficient of segmentation (up to 0.602), and is superior to a separate 2D segmentation model. Compared with a 3D segmentation model, the application maintains similar segmentation performance and is shorter in reasoning time. The application can simultaneously complete the classification and segmentation tasks for positive lymph nodes in a CT image, reduces the work burden of doctors, improves the diagnosis and treatment efficiency of doctors, and fills the blank of simultaneously judging and outlining positive lymph nodes in a head and neck CT image of a nasopharyngeal carcinoma patient.

Provided is a method (100) to generate a 3-dimensional segmentation of cells based on a stack of images taken at different focal planes within a specimen. The method comprises receiving a 2-dimensional vertical gradient map and a 2-dimensional horizontal gradient map for every focal plane (110) and determining a 2-dimensional cell segmentation map (120) for every focal plane based on the vertical gradient map and the horizontal gradient map of the focal plane. The method further comprises merging 2-dimensional cell segmentation maps of neighboring focal planes (130) to generate the 3-dimensional segmentation of the cells.

Unsupervised volumetric 3D animation (UVA) of non-rigid deformable objects without annotations learns the 3D structure and dynamics of objects solely from single-view red / green / blue (RGB) videos and decomposes the single-view RGB videos into semantically meaningful parts that can be tracked and animated. Using a 3D autodecoder framework, paired with a keypoint estimator via a differentiable perspective-n-point (PnP) algorithm, the UVA model learns the underlying object 3D geometry and parts decomposition in an entirely unsupervised manner from still or video images. This allows the UVA model to perform 3D segmentation, 3D keypoint estimation, novel view synthesis, and animation. The UVA model can obtain animatable 3D objects from a single or a few images. The UVA method also features a space in which all objects are represented in their canonical, animation-ready form. Applications include the creation of lenses from images or videos for social media applications.

The invention discloses a three-dimensional medical image segmentation system and method based on three-dimensional structure enhancement. Belongs to the technical field of medical image processing. The technical problem that when an RWKV framework is directly applied to a medical image three-dimensional segmentation task, three-dimensional morphological characteristics of organs are difficult to accurately describe is solved. The system comprises an encoder and a decoder, the encoder comprises a plurality of down-sampling layers containing three-way spatial enhancement modules, and the decoder comprises up-sampling layers containing three-way spatial enhancement modules, the number of the up-sampling layers being the same as the number of the up-sampling layers in the encoder. The three-way space enhancement module in the code is connected with the corresponding three-way space enhancement module in the decoder through jump connection; the three-way space enhancement module is formed by improving an existing RWKV module, and the improvement points comprise the steps that the serialization modeling operation of the RWKV module is modified from a single sequence direction to three sequence directions; spatial shift operation is added to the data input ends of the two hybrid modules of the RWKV module.

The invention discloses a CT aorta robustness three-dimensional segmentation method and device based on deep learning, and relates to the technical field of medical image processing, and the method comprises the steps: extracting an aorta slice sequence frame by frame; carrying out linear interpolation and thresholding on the extracted probability graph; setting the initial segmentation mask of the current slice as an initialization prompt of a medical video segmentation model with a memory mechanism; for the initial slice image, calling an initialized memory state function through a medical video segmentation model, generating an initial segmentation result of the initial slice, and encoding the initial segmentation result into an initial memory state; calling a propagation tracking state function to automatically predict an aorta original mask of a current slice through a medical video segmentation model by using memory states established in previous several frames of slices and a current slice image, and obtaining a prediction mask; and carrying out weighted fusion and three-dimensional reconstruction on the prediction mask and the preliminary segmentation mask. According to the method, full-automatic and robust three-dimensional segmentation of the CT aorta is realized.

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 provides a human skin sweat gland three-dimensional shape obtaining method and system and electronic equipment, and relates to the technical field of optical coherence tomography and medical image segmentation, and the method comprises the steps: obtaining an initial to-be-detected region skin OCT image through a spectral domain optical coherence tomography system; preprocessing the skin OCT image of the initial to-be-detected area, wherein preprocessing comprises but is not limited to one or more of cutting, overturning, size adjustment and contrast adjustment; inputting the preprocessed skin OCT image of the initial to-be-detected area into a three-dimensional segmented skin sweat gland network model trained in advance based on deep learning, and performing three-dimensional segmentation on the preprocessed skin OCT image of the initial to-be-detected area by the deep learning model to obtain a three-dimensional segmentation result of sweat glands; according to the three-dimensional shape obtaining method, the three-dimensional shape information of the sweat gland of the human skin can be obtained in a real-time and non-invasive mode, the two-dimensional, in-vitro and destructive limitation of a traditional skin biopsy technology is overcome, and the accuracy and efficiency of sweat gland shape analysis are remarkably improved.