Deep learning in cortical surface-based neuroimage analysis:a systematic review  

作　　者：Fenqiang Zhao Zhengwang Wu Gang Li 

机构地区：[1]Department of Radiology and Biomedical Research Imaging Center,University of North Carolina at Chapel Hill,NC 27599,United States

出　　处：《Intelligent Medicine》2023年第1期46-58,共13页智慧医学（英文）

基　　金：the National Institutes of Health(NIH)(Grant Nos.MH116225,MH117943,MH123202,and AG075582).

摘　　要：Deep learning approaches,especially convolutional neural networks(CNNs),have become the method of choice in the field of medical image analysis over the last few years.This prevalence is attributed to their excellent abilities to learn features in a more effective and efficient manner,not only for 2D/3D images in the Euclidean space,but also for meshes and graphs in non-Euclidean space such as cortical surfaces in neuroimaging analysis field.The brain cerebral cortex is a highly convoluted and thin sheet of gray matter(GM)that is thus typically represented by triangular surface meshes with an intrinsic spherical topology for each hemisphere.Accordingly,novel tailored deep learning methods have been developed for cortical surface-based analysis of neuroimaging data.This paper reviewsed the representative deep learning techniques relevant to cortical surface-based analysis and summarizes recent major contributions to the field.Specifically,we surveyed the use of deep learning techniques for cortical surface reconstruction,registration,parcellation,prediction,and other applications.We concluded by discussing the open challenges,limitations,and potentials of these techniques,and suggested directions for future research.

关 键 词：Deep learning Cortical surface-based analysis Neuroimage analysis Reconstruction REGISTRATION PARCELLATION 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] TP18[自动化与计算机技术—计算机科学与技术]