公开(公告)号：US20230368896A1

公开(公告)日：2023-11-16

申请号：US18183931

申请日：2023-03-14

Applicant: SOUTH CHINA NORMAL UNIVERSITY

Inventor： Qi YE ,  Lihui WEN ,  Jiawei CHEN ,  Chihua FANG

IPC: G16H30/40 ,  G06T7/10 ,  G06T3/40

CPC classification number: G16H30/40 ,  G06T7/10 ,  G06T3/4007 ,  G06T2207/20084

Abstract: The present invention provides a medical image segmentation method based on a Boosting-Unet segmentation network. By dividing training of an overall segmentation network into training of m sub segmentation networks, the method inherits convolution kernel parameters of the (k−1)th sub segmentation network during training of the kth sub segmentation network, thereby greatly decreasing the quantity of the convolution kernel parameters during every training and improving the learning ability of the network and the resistance to noise and image blur. In addition, a plurality of sub segmentation networks are arranged, so that the efficiency of the network is improved, a depth of an image data feature is also extracted, and the image data is segmented precisely, thereby improving the learning ability of the overall segmentation network to the image data feature, enhancing the robustness to noise disturbance information and further improving the performance of image segmentation.