5 results about "Anatomical atlas" patented technology

This invention provides an injection path guidance method, system, device, and medium based on ultrasound and laser combined navigation, belonging to the field of medical injection technology. The method includes: denoising an initial sound wave to obtain a denoised wave; identifying the boundary points of the denoised wave based on the mean square relative fitting error to obtain an anatomical structure image of the target muscle; performing image segmentation on the anatomical structure image to obtain a target structure image of the target muscle; extracting feature information at different scales from the target structure image and then performing adaptive clustering through variable convolution to obtain a three-dimensional result of the target; registering and comparing the target three-dimensional result with an anatomical atlas database, marking key avoidance structures to determine a safe puncture boundary; determining the puncture target point and alternative paths in the target three-dimensional result based on the safe puncture boundary; and projecting light spot markers and the target path onto the target muscle using a near-infrared laser navigation module based on spatial transformation relationships between the puncture target point and the alternative paths.

The application relates to a multi-region identification method, device and equipment of an endoscope image and a storage medium, wherein the multi-region identification method of the endoscope image comprises the following steps: projecting a three-dimensional anatomical atlas matched with an endoscope image to the endoscope image, and generating a region mask of the endoscope image according to a projection result; wherein the region mask is used for indicating an anatomical region corresponding to each pixel position in the endoscope image; based on the region mask, a constraint mask of each anatomical region is generated; based on a first image feature of the endoscope image and the region mask, a target image feature of the endoscope image is determined; the target image feature is enhanced based on the constraint mask of each anatomical region, and region identification is performed on the endoscope image based on the enhanced target image feature. Through the application, the problem that accurate identification of multiple anatomical regions in an endoscope image is difficult to realize is solved, and accurate identification of multiple anatomical regions in an endoscope image is realized.

The invention discloses a tumor recurrence and metastasis risk prediction method and system based on an anatomical map. The method comprises the following steps: acquiring a patient image report, a complaint text and non-equidistant tumor marker data; based on a tumor metastasis knowledge graph, mapping an image report and a chief complaint text to corresponding anatomical nodes, calculating semantic alignment weights of objective lesions and subjective symptoms on the nodes by using a cross attention mechanism, and generating multi-modal fusion node features; utilizing a continuous time model to extract biochemical trend characteristics of the marker data as a global state variable injection map; and finally, inputting the multi-modal fusion node features and the global state variables into a graph neural network for feature aggregation and reasoning, and outputting a risk prediction result and a high-risk anatomical region. According to the invention, early-stage accurate early warning and interpretable path reasoning of tumor recurrence and metastasis risks are realized.

The application discloses a tumor recurrence and metastasis risk prediction method and system based on an anatomical atlas, and the method comprises the following steps: acquiring a patient image report, a chief complaint text and non-equidistant tumor marker data; based on a tumor metastasis knowledge graph, the image report and the chief complaint text are mapped to corresponding anatomical nodes, the semantic alignment weight of objective lesions and subjective symptoms on the nodes is calculated by using a cross-attention mechanism, and multi-modal fusion node features are generated; the biochemical trend features of the marker data are extracted as global state variables by using a continuous time model to inject the graph; finally, the multi-modal fusion node features and the global state variables are input into a graph neural network for feature aggregation and reasoning, and a risk prediction result and a high-risk anatomical region are output. The application realizes early and accurate early warning and an interpretable path reasoning for tumor recurrence and metastasis risk.

The invention relates to the technical field of ultrasonic diagnosis, in particular to an abdominal ultrasonic multi-modal image fusion analysis system which comprises a multi-modal space alignment module, an anatomical semantic structuring module, a multi-modal feature fusion and visualization module, a multi-parameter quantitative analysis module, a qualitative evaluation matrix construction module and an intelligent report generation module. By taking the gray-scale ultrasound image sequence of the target abdominal region as a space reference, aligning the elastography and ultrasound contrast sequences to obtain a multi-modal image set; mapping an anatomical structure, tissue hardness and blood perfusion information in the image set to a standard anatomical map to generate structured data; associating and visually coding the tissue hardness distribution information and the blood perfusion dynamic information to obtain a fused image; feature parameters of the fused image are analyzed and compared with a clinical threshold value to obtain quantitative parameters, and a qualitative evaluation matrix is constructed; summarizing qualitative evaluation indexes, quantitative parameters and structured description data to generate an analysis report; the efficiency of abdominal ultrasonic diagnosis analysis can be improved.