Cortical Surface Shape Analysis Based on Alexandrov Polyhedra

Min Zhang, Yang Guo, Na Lei, Zhou Zhao, Jianfeng Wu, Xiaoyin Xu, Yalin Wang, Xianfeng Gu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationProceedings - 2021 IEEE/CVF International Conference on Computer Vision, ICCV 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages14224-14232
Number of pages9
ISBN (Electronic)9781665428125
DOIs
StatePublished - 2021
Externally publishedYes
Event18th IEEE/CVF International Conference on Computer Vision, ICCV 2021 - Virtual, Online, Canada
Duration: Oct 11 2021Oct 17 2021

Publication series

NameProceedings of the IEEE International Conference on Computer Vision
ISSN (Print)1550-5499

Conference

Conference18th IEEE/CVF International Conference on Computer Vision, ICCV 2021
Country/TerritoryCanada
CityVirtual, Online
Period10/11/2110/17/21

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

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