Abstract: Provided is an image and segmentation label generative model for tree-structured data and application. The model includes a simulation model for tree-structured image designed based on a small amount of expert knowledge and a generative network model based on morphological loss function. The present application can generate a simulated tree-structured image with labels that have a style highly similar to that of the real target image. This technology can generate image and segmentation labels for tree target in various medical images, such as brain neurons, retinal vessels, and trachea in the lungs, and the data quality can reach the level of manually annotated data. The model according to the present disclosure is the first model with the ability to automatically generate segmentation level data, and has the advantages of simple and flexible model configuration, high-quality generated data, and a wide range of applications.

Abstract: A method for extracting significant texture features of a B-ultrasonic image and application thereof discloses a channel attention mechanism network, i.e. a context activation residual network, which is designed to effectively model the B-ultrasonic liver fibrosis texture information, and which uses the global context information to strengthen important texture features and suppress useless texture features, such that the deep residual network can capture more significant texture information in the B-ultrasonic images. The process can be mainly divided into two phases: training and testing. During the training phase, the context activation residual network may he trained by using the B-ultrasonic image blocks as input and the pathological results of liver biopsy as labels. During the testing phase, the B-ultrasonic image blocks may be input into the trained non-invasive liver fibrosis diagnosis model to obtain the liver fibrosis staging result for each ultrasonic image.