Recurrent Tissue-Aware Network for Deformable Registration of Infant Brain MR Images.

ABSTRACT: Deformable registration is fundamental to longitudinal and population-based image analyses. However, it is challenging to precisely align longitudinal infant brain MR images of the same subject, as well as cross-sectional infant brain MR images of different subjects, due to fast brain development during infancy. In this paper, we propose a recurrently usable deep neural network for the registration of infant brain MR images. There are three main highlights of our proposed method. (i) We use brain tissue segmentation maps for registration, instead of intensity images, to tackle the issue of rapid contrast changes of brain tissues during the first year of life. (ii) A single registration network is trained in a one-shot manner, and then recurrently applied in inference for multiple times, su

SUBMITTER: Wei D 

PROVIDER: S-EPMC9064923 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature