3D segmentation of glial cells using fully convolutional networks and k-terminal cut

Lin Yang, Yizhe Zhang, Ian H. Guldner, Siyuan Zhang, Danny Z. Chen

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

21 Scopus citations

Abstract

Glial cells play an important role in regulating synaptogenesis,development of blood-brain barrier,and brain tumor metastasis. Quantitative analysis of glial cells can offer new insights to many studies. However,the complicated morphology of the protrusions of glial cells and the entangled cell-to-cell network cause significant difficulties to extracting quantitative information in images. In this paper,we present a new method for instance-level segmentation of glial cells in 3D images. First,we obtain accurate voxel-level segmentation by leveraging the recent advances of fully convolutional networks (FCN). Then we develop a kterminal cut algorithm to disentangle the complex cell-to-cell connections. During the cell cutting process,to better capture the nature of glial cells,a shape prior computed based on a multiplicative Voronoi diagram is exploited. Extensive experiments using real 3D images show that our method has superior performance over the state-of-the-art methods.

Original languageEnglish (US)
Title of host publicationMedical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings
EditorsGozde Unal, Sebastian Ourselin, Leo Joskowicz, Mert R. Sabuncu, William Wells
PublisherSpringer Verlag
Pages658-666
Number of pages9
ISBN (Print)9783319467221
DOIs
StatePublished - 2016
Externally publishedYes

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9901 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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