Surface Foliation Based Brain Morphometry Analysis

Chengfeng Wen, Na Lei, Ming Ma, Xin Qi, Wen Zhang, Yalin Wang, Xianfeng Gu

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

Abstract

Brain morphometry plays a fundamental role in neuroimaging research. In this work, we propose a novel method for brain surface morphometry analysis based on surface foliation theory. Given brain cortical surfaces with automatically extracted landmark curves, we first construct finite foliations on surfaces. A set of admissible curves and a height parameter for each loop are provided by users. The admissible curves cut the surface into a set of pairs of pants. A pants decomposition graph is then constructed. Strebel differential is obtained by computing a unique harmonic map from surface to pants decomposition graph. The critical trajectories of Strebel differential decompose the surface into topological cylinders. After conformally mapping those topological cylinders to standard cylinders, parameters of standard cylinders (height, circumference) are intrinsic geometric features of the original cortical surfaces and thus can be used for morphometry analysis purpose. In this work, we propose a set of novel surface features. To the best of our knowledge, this is the first work to make use of surface foliation theory for brain morphometry analysis. The features we computed are intrinsic and informative. The proposed method is rigorous, geometric, and automatic. Experimental results on classifying brain cortical surfaces between patients with Alzheimer’s disease and healthy control subjects demonstrate the efficiency and efficacy of our method.

Original languageEnglish (US)
Title of host publicationMultimodal Brain Image Analysis and Mathematical Foundations of Computational Anatomy - 4th International Workshop, MBIA 2019, and 7th International Workshop, MFCA 2019, Held in Conjunction with MICCAI 2019, Proceedings
EditorsDajiang Zhu, Jingwen Yan, Heng Huang, Li Shen, Paul M. Thompson, Carl-Fredrik Westin, Xavier Pennec, Sarang Joshi, Mads Nielsen, Stefan Sommer, Tom Fletcher, Stanley Durrleman
PublisherSpringer
Pages186-195
Number of pages10
ISBN (Print)9783030332259
DOIs
StatePublished - Jan 1 2019
Event4th International Workshop on Multimodal Brain Image Analysis, MBAI 2019, and the 7th International Workshop on Mathematical Foundations of Computational Anatomy, MFCA 2019, held in conjunction with the 22nd International Conference on Medical Imaging and Computer Assisted Intervention, MICCAI 2019 - Shenzhen, China
Duration: Oct 17 2019Oct 17 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11846 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference4th International Workshop on Multimodal Brain Image Analysis, MBAI 2019, and the 7th International Workshop on Mathematical Foundations of Computational Anatomy, MFCA 2019, held in conjunction with the 22nd International Conference on Medical Imaging and Computer Assisted Intervention, MICCAI 2019
CountryChina
CityShenzhen
Period10/17/1910/17/19

Keywords

  • Alzheimer disease
  • Brain morphometry
  • Shape classification
  • Surface foliation

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Wen, C., Lei, N., Ma, M., Qi, X., Zhang, W., Wang, Y., & Gu, X. (2019). Surface Foliation Based Brain Morphometry Analysis. In D. Zhu, J. Yan, H. Huang, L. Shen, P. M. Thompson, C-F. Westin, X. Pennec, S. Joshi, M. Nielsen, S. Sommer, T. Fletcher, & S. Durrleman (Eds.), Multimodal Brain Image Analysis and Mathematical Foundations of Computational Anatomy - 4th International Workshop, MBIA 2019, and 7th International Workshop, MFCA 2019, Held in Conjunction with MICCAI 2019, Proceedings (pp. 186-195). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11846 LNCS). Springer. https://doi.org/10.1007/978-3-030-33226-6_20