2 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 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.