Topological correction of infant white matter surfaces using anatomically constrained convolutional neural network.

ABSTRACT: Reconstruction of accurate cortical surfaces without topological errors (i.e., handles and holes) from infant brain MR images is very important in early brain development studies. However, infant brain MR images typically suffer extremely low tissue contrast and dynamic imaging appearance patterns. Thus, it is inevitable to have large amounts of topological errors in the segmented infant brain tissue images, which lead to inaccurately reconstructed cortical surfaces with topological errors. To address this issue, inspired by recent advances in deep learning, we propose an anatomically constrained network for topological correction on infant cortical surfaces. Specifically, in our method, we first locate regions of potential topological defects by leveraging a topology-preserving level set

SUBMITTER: Sun L 

PROVIDER: S-EPMC6602545 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature