公开(公告)号：US11593942B2

公开(公告)日：2023-02-28

申请号：US17620756

申请日：2021-04-12

Applicant: NANTONG UNIVERSITY

Inventor： Weiping Ding ,  Zhihao Feng ,  Ming Li ,  Ying Sun ,  Yi Zhang ,  Hengrong Ju ,  Jinxin Cao

IPC: G06T7/00 ,  G06T7/10 ,  G16H30/40 ,  G06V10/82 ,  G06V10/776

Abstract: Disclosed is a fully convolutional genetic neural network method for segmentation of infant brain record images. First, infant brain record image data is input and preprocessed, and genetic coding initialization is performed for parameters according to the length of a DMPGA-FCN network weight. Then, m individuals are randomly grouped into genetic native subpopulations and corresponding twin subpopulations are derived, where respective crossover probability and mutation probability pm of all the subpopulations are determined from disjoint intervals; and an optimal initialization value fa is searched for by using a genetic operator. Afterwards, fa is used as a forward propagation calculation parameter and a weighting operation is performed on the feature address featuremap. Finally, a pixel-by-pixel cross-entropy loss is calculated between predicted infant brain record images and standard segmented images to reversely update the weights, thus finally obtaining optimal weights of a network model for segmentation of the infant brain record images.