Abstract
The significance of the transentorhinal (TE) cortex has been well known for the early diagnosis of Alzheimer's disease (AD). However, precise mapping of the TE cortex for the detection of local changes in the region was not well established mostly due to significant geometric variations around TE. In this paper, we propose a novel framework for automated patch generation of the TE cortex, patch-based mapping, and construction of an atlas with a distributed network. We locate the TE cortex and extract a small patch surrounding the TE cortex from a cortical surface using a coarse map by FreeSurfer. We apply a recently developed intrinsic surface mapping algorithm based on Riemannian metric optimization on surfaces (RMOS) in the Laplace-Beltrami embedding space to compute fine maps between the small patches. We also develop a distributed atlas of the TE cortex, formed by a shortest path tree whose nodes are atlas subjects, to reduce anatomical misalignments by mapping only between similar patches. In our experimental results, we construct the distributed atlas of the TE cortex using 50 subjects from the Human Connectome Project (HCP), and show that detailed correspondences within the distributed network are established. Using a large-scale dataset of 380 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI), we demonstrate that our patch-based mapping with the distribute atlas outperforms the conventional centralized mapping (direct mapping to a single atlas) for detecting atrophy of the TE cortex in the early stage of AD.