Improving human cortical sulcal curve labeling in large scale cross-sectional MRI using deep neural networks.

ABSTRACT:

Background

Human cortical primary sulci are relatively stable landmarks and commonly observed across the population. Despite their stability, the primary sulci exhibit phenotypic variability.

New method

We propose a fully automated pipeline that integrates both sulcal curve extraction and labeling. In this study, we use a large normal control population (n = 1424) to train neural networks for accurately labeling the primary sulci. Briefly, we use sulcal curve distance map, surface parcellation, mean curvature and spectral features to delineate their sulcal labels. We evaluate the proposed method with 8 primary sulcal curves in the left and right hemispheres compared to an established multi-atlas curve labeling method.

Results

Sulcal labels by the proposed method reaso

SUBMITTER: Parvathaneni P 

PROVIDER: S-EPMC6663093 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature