5 results about "Lung segmentation" patented technology

The invention provides an nnunet-based lung eighteen-segment segmentation and post-processing correction method and system, which are applied to the technical field of medical image processing, and are characterized in that left and right lung segmentation is carried out on CT image data by adopting a lightweight left and right lung segmentation model, the left and right lungs are independently processed through region cutting, and finally, a hierarchical post-processing correction mechanism is introduced, so that the correction accuracy of the lung eighteen-segment segmentation and post-processing correction is improved. And the anatomical rationality and accuracy of the segmentation result are ensured. In conclusion, by optimizing the model structure and introducing the refined post-processing correction strategy, the lung eighteen-segment segmentation efficiency is improved, the resource consumption is reduced, the accuracy and reliability of the segmentation result are remarkably improved, and a better technical solution is provided for clinical medical image analysis.

The invention provides an AI-based medical respiratory system diagnosis auxiliary method and system, and the method comprises the steps: obtaining a chest CT image from a PACS system, carrying out the preprocessing of lung segmentation, window level adjustment and the like, and generating a concept score vector through a concept activation encoder; the method specifically comprises the steps that a backbone network extracts a preprocessed image feature map, similarity is calculated according to the feature map and a predefined concept prototype library to generate an activation map, a high activation area of each concept prototype is determined, and average similarity is calculated to obtain a concept score; a diagnosis probability is generated by a microcosmic concept inference device, that is, output is generated by applying logic atoms (weight is set based on medical priori knowledge), and the diagnosis probability is generated through a classification layer in combination with a concept score vector; and finally, generating a structured report according to the diagnosis probability and the concept score vector. According to the method, diagnosis is decoupled into concept discovery, quantification and reasoning, interpretable decision making is achieved, the black box problem is solved, and a doctor can conveniently trace the AI judgment basis.

Devices, methods, and systems are provided for analyzing lung imaging data. A server computing device receives imaging data of a lung over a network from a client computing device and analyzes the imaging data to identify lung, airways, and blood vessels, segment the lung into lobes, subtract airways, calculate volumes, calculate emphysema scores, identify fissure locations, calculate fissure completeness scores. A reconstruction of the fissures indicating locations where the fissures are incomplete and a report comprising fissure scores, volumes, and emphysema scores are created.

The present disclosure relates to a lung segmentation, lung disease evaluation method and device, electronic equipment and storage medium, and relates to the technical field of DR lung image segmentation. The segmentation method comprises: acquiring a segmentation model of a preset convolutional neural network, a DR lung region label image used for training the segmentation model, and a plurality of DR lung images to be segmented at multiple moments in a breathing process or a breath-holding state; wherein the method for determining the DR lung region label image used for training the segmentation model comprises: respectively performing rib edge boundary, lung apex boundary, and mediastinum and diaphragm edge detection on left chest images and right chest images of the plurality of DR lung region images to obtain the DR lung region label image; training the segmentation model by using the DR lung region label image; and completing left lung and / or right lung segmentation of the plurality of DR lung images to be segmented based on the trained segmentation model. Lung region segmentation and lung disease evaluation of DR lung images can be realized.

The application provides a kind of based on double knowledge graph's pneumoconiosis X-ray chest radiograph classification method, device, equipment and storage medium, it is related to medical image processing technical field.The method comprises: after lung segmentation is carried out to chest radiograph, utilize the frozen CLIP visual encoder and its embedded multilevel residual adapter output multistage enhanced visual features to strengthen subtle lesion expression;Based on medical classification standard, construct text and visual double knowledge subgraph, carry out graph convolution interaction and fusion to original text features and subgraph, obtain optimized text features;Visual and text features are mapped to unified semantic space, the similarity of hierarchical alignment is calculated, and the classification result is obtained by attention mechanism fusion.The present application introduces medical priori through knowledge graph, combined with multilevel visual enhancement, can significantly improve the recognition ability of pneumoconiosis lesion and cross-modal semantic alignment accuracy, realize high-accuracy automatic classification.