Geometric Brain Surface Network for Brain Cortical Parcellation

Wen Zhang, Yalin Wang

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

Abstract

A large number of surface-based analyses on brain imaging data adopt some specific brain atlases to better assess structural and functional changes in one or more brain regions. In these analyses, it is necessary to obtain an anatomically correct surface parcellation scheme in an individual brain by referring to the given atlas. Traditional ways to accomplish this goal are through a designed surface-based registration or hand-crafted surface features, although both of them are time-consuming. A recent deep learning approach depends on a regular spherical parameterization of the mesh, which is computationally prohibitive in some cases and may also demand further post-processing to refine the network output. Therefore, an accurate and fully-automatic cortical surface parcellation scheme directly working on the original brain surfaces would be highly advantageous. In this study, we propose an end-to-end deep brain cortical parcellation network, called DBPN. Through intrinsic and extrinsic graph convolution kernels, DBPN dynamically deciphers neighborhood graph topology around each vertex and encodes the deciphered knowledge into node features. Eventually, a non-linear mapping between the node features and parcellation labels is constructed. Our model is a two-stage deep network which contains a coarse parcellation network with a U-shape structure and a refinement network to fine-tune the coarse results. We evaluate our model in a large public dataset and our work achieves superior performance than state-of-the-art baseline methods in both accuracy and efficiency.

Original languageEnglish (US)
Title of host publicationGraph Learning in Medical Imaging - 1st International Workshop, GLMI 2019, held in Conjunction with MICCAI 2019, Proceedings
EditorsDaoqiang Zhang, Luping Zhou, Biao Jie, Mingxia Liu
PublisherSpringer
Pages120-129
Number of pages10
ISBN (Print)9783030358167
DOIs
StatePublished - Jan 1 2019
Event1st International Workshop on Graph Learning in Medical Imaging, GLMI 2019 held in conjunction with the 22nd International Conference on Medical Image Computing 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)
Volume11849 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference1st International Workshop on Graph Learning in Medical Imaging, GLMI 2019 held in conjunction with the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019
CountryChina
CityShenzhen
Period10/17/1910/17/19

Keywords

  • Brain cortical surface
  • Deep learning
  • Geometry
  • Parcellation

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Zhang, W., & Wang, Y. (2019). Geometric Brain Surface Network for Brain Cortical Parcellation. In D. Zhang, L. Zhou, B. Jie, & M. Liu (Eds.), Graph Learning in Medical Imaging - 1st International Workshop, GLMI 2019, held in Conjunction with MICCAI 2019, Proceedings (pp. 120-129). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11849 LNCS). Springer. https://doi.org/10.1007/978-3-030-35817-4_15