9 results about "Bone segmentation" patented technology

This invention discloses a fully automated quality control method for knee joint DR radiographs based on multi-task deep learning and geometric quantization. The method includes: using DICOM format knee joint DR images as input data and performing data preprocessing; constructing a multi-task model architecture comprising a knee joint anteroposterior and lateral bone segmentation model, a knee joint anteroposterior and lateral keypoint detection model, a foreign object detection model, and a left / right marker detection model, defining the network infrastructure and input / output of each model; calculating quality control-related indicators based on the bone mask, keypoints, foreign object detection results, and left / right marker detection results output by the four models; formulating scoring rules to score the quality of knee joint DR images, and performing automatic quality control of knee joint DR images. This invention achieves objective, efficient, and quantifiable knee joint DR image quality assessment by automatically extracting anatomical structures, calculating geometric parameters, and executing scoring rules, providing a reusable technical paradigm.

Embodiments of the present application disclose an image segmentation method and device, a storage medium and an electronic device, and relate to the technical field of image processing. The method comprises: acquiring CTA image data; performing first bone segmentation processing on the image data to obtain a first bone segmentation result; performing cropping on the image data according to the first bone segmentation result to obtain first cropped image data comprising only a region above the shoulder; performing second bone segmentation processing on the first cropped image data to obtain a second bone segmentation result; and performing fusion processing on the first bone segmentation result and the second bone segmentation result to obtain a fused bone result. The present application significantly improves the segmentation accuracy of head and neck bone tissue by cascading two bone segmentation processing procedures with different precisions and different inputs.

This application discloses an automatic analysis method for CT images of long bones in mice and rats, relating to the field of image processing technology. The method includes: acquiring a CT image sequence of the target long bone, extracting the three-dimensional spatial distribution of bone tissue, and performing pose alignment and three-dimensional cropping to obtain a standardized bone image sequence; then constructing candidate depth intervals based on butterfly-shaped anatomical features and key points of growth plate strip-shaped gaps, respectively, and obtaining a fine search range through intersection operation; subsequently identifying candidate slices of triangular fractures and determining the anatomical zero point; finally, mapping the target analysis region based on the anatomical zero point, completing cortical bone segmentation, medullary cavity segmentation, and trabecular bone extraction under closed mask constraints, and outputting quantitative parameters of bone tissue. This application achieves accurate positioning of the growth plate and unified analysis region mapping in long bone CT images, improving the accuracy of trabecular bone extraction and the consistency of bone tissue quantification results.

The application discloses a knee cartilage segmentation prediction visualization method based on Mamba and three-dimensional attention, and belongs to the fields of deep learning and medical image processing. The method comprises the following steps: standardizing and pre-processing knee MRI three-dimensional data sets and performing data enhancement to construct training samples; a three-dimensional segmentation network TriAttnMamba3D-Net with an encoder-decoder structure is constructed, the encoder of the three-dimensional segmentation network TriAttnMamba3D-Net is embedded with a balanced Mamba module to efficiently model long-range spatial dependence, a three-dimensional directional attention is integrated in a bottleneck layer to enhance the feature expression of thin layer boundaries and cartilage regions, the decoder is fused with multi-scale features through a skip connection, and a composite loss function is adopted for optimization; and the processed data is loaded into the network for end-to-end training to generate a segmentation model. The application has the advantages of advanced nature and clinical conversion value, and can guarantee the accuracy while improving the calculation efficiency in view of problems such as the thin layer structure of cartilage, low contrast and multiple tissue adhesions.

The application provides a skeleton segmentation method and device, electronic equipment and storage medium. The method comprises the following steps: acquiring multi-energy spectrum data of a scanning object collected by a CT device; generating a virtual single-energy graph of a target single energy level based on the multi-energy spectrum data; and performing skeleton segmentation based on the virtual single-energy graph. The method solves the technical pain point that the conventional bone segmentation technology is highly sensitive to HU (Hounsfield Unit) value instability, uses the virtual single-energy graph generated by the multi-energy spectrum data as a skeleton segmentation data source, avoids the problem that the HU value in the conventional CT image is easily affected by the scanning protocol and device model and fluctuates, and improves the stability and accuracy of the skeleton segmentation. The virtual single-energy graph of the target single energy level can effectively improve the density differentiation degree of the bone tissue and the soft tissue, reduces the interference of the soft tissue, and makes the extraction of the skeleton region more accurate.

The application provides a knee cartilage segmentation method based on Mamba state space model and dynamic hypergraph feature modeling. The method comprises three stages of data preprocessing, three-dimensional feature learning and segmentation prediction. First, the knee MRI data is preprocessed, and the model generalization ability is improved by combining data enhancement. Then, a network is constructed, the convolution and Mamba module are fused in the encoding stage to extract local and long-range dependent features, and the VSS module is introduced to strengthen the global context information; in the decoding stage, multi-scale features are fused through the jump connection, and the gating mechanism is added in the high-level semantics to realize the adaptive selection of features. Further, the dynamic hypergraph module is introduced in the deep feature fusion to model the complex spatial topological relationship of the cartilage area and improve the fine-grained structure expression ability. The method effectively improves the problems of fuzzy cartilage boundary, complex structure and class imbalance, and significantly improves the segmentation accuracy and stability while ensuring the computing efficiency.

The present application relates to a kind of spinal lamina autonomous cutting and its cut through and stop perception method and device, wherein the method comprises: obtaining initial lamina structure data set input to lamina bone segmentation network model, output lamina bone tissue segmentation result and probability distribution diagram of dural sac safety termination surface;According to lamina bone tissue segmentation result, rigid registration relationship constructs millimeter level cuttable feasible region;With probability distribution diagram as the insurmountable safety limit of millimeter level cuttable feasible region, generate six degrees of freedom tool center point motion trajectory, obtain cutting path planning data;According to cutting path planning data, execute autonomous cutting operation under impedance control;According to the determination result, trigger cut-through determination flag signal;Bottom motion controller responds to cut-through determination flag signal.The present application generates six degrees of freedom tool center point motion trajectory by setting probability distribution diagram of dural sac safety termination surface as insurmountable safety limit, significantly reduces the risk of surgery.

This invention belongs to the interdisciplinary field of medical image processing and artificial intelligence, and relates to a method for oral and maxillofacial bone segmentation based on multi-scale kernel cross-band interactive attention fusion. It involves acquiring hyperspectral images through a standardized hyperspectral oral and maxillofacial bone data acquisition platform and constructing a training dataset with pathological gold standard annotations. A multi-scale kernel cross-band interactive attention fusion segmentation model is constructed, integrating multi-scale feature extraction, hierarchical attention optimization, and multi-level complexity adaptation mechanisms. The model is trained based on the annotated dataset, and a converged hyperspectral oral and maxillofacial bone segmentation network is obtained through multi-scale supervision and loss function optimization. The trained model is deployed to clinical scenarios to segment hyperspectral oral and maxillofacial bone images and output results, providing support for diagnostic and treatment decisions. This invention effectively solves the problems of insufficient accuracy, limited deployment, and poor scenario adaptability in oral and maxillofacial bone segmentation, providing an efficient and reliable image analysis tool for oral and maxillofacial surgery.

This invention discloses a WTNet pediatric mandibular wisdom tooth germ segmentation network architecture method, mainly comprising an input enhancement module with a region feature enhancement module and a bone feature separation module. Independent scale-specific feature fusion modules are provided in the tooth segmentation branch and the bone segmentation branch of the bone feature separation module, respectively. For an input and its corresponding ground truth label, the input is first fed into the input enhancement module to generate a mask and supervised using the ground truth label; then, the input and mask are fed into the region feature enhancement module to obtain enhanced input; in the bone feature separation module, the enhanced input is fed into the scale-specific feature fusion modules of the tooth segmentation branch and the bone segmentation branch respectively after passing through a shared encoder, and then fed into the decoders of the two branches. The tooth and bone ground truth labels are used to complete the supervision of the two branches respectively, obtaining tooth segmentation masks and bone segmentation masks. The two masks are fused to obtain the final segmentation result.