Cortical Surface Shape Analysis Based on Alexandrov Polyhedra
Year: 2021
Type: conference-paper
Abstract: Shape analysis has been playing an important role in early diagnosis and prognosis of neurodegenerative diseases such as Alzheimer's diseases (AD). However, obtaining effective shape representations remains challenging. This paper proposes to use the Alexandrov polyhedra as surface-based shape signatures for cortical morphometry analysis. Given a closed genus-0 surface, its Alexandrov polyhedron is a convex representation that encodes its intrinsic geometry information. We propose to compute the polyhedra via a novel spherical optimal transport (OT) computation. In our experiments, we observe that the Alexandrov polyhedra of cortical surfaces between pathology-confirmed AD and cognitively unimpaired individuals are significantly different. Moreover, we propose a visualization method by comparing local geometry differences across cortical surfaces. We show that the proposed method is effective in pinpointing regional cortical structural changes impacted by AD.
Authors: Min Zhang, Yang Guo, Na Lei, Zhou Zhao, Jianfeng Wu, Xiaoyin Xu, Yalin Wang, Xianfeng Gu
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: Predicting the early childhood outcomes of preterm brain shape abnormalities, Hierarchical Bayesian Analysis of Retinotopic Maps of the Human Visual Cortex with Conformal Geometry, Developing a Univariate Neurodegeneration Imaging Biomarker with Optimal Transportation, Multi-Source Sparse Learning to Identify MCI and Predict Decline, Early joint cranial and brain development from fetal and pediatric imaging
OpenAlex ID: W3202131567