Effective nuclei segmentation with sparse shape prior and dynamic occlusion constraint for glioblastoma pathology images

Pengyue Zhang, Fusheng Wang, George Teodoro, Yanhui Liang, Mousumi Roy, Daniel Brat, Jun Kong*

*Corresponding author for this work

Research output: Contribution to journalArticle

Abstract

We propose a segmentation method for nuclei in glioblastoma histopathologic images based on a sparse shape prior guided variational level set framework. By spectral clustering and sparse coding, a set of shape priors is exploited to accommodate complicated shape variations. We automate the object contour initialization by a seed detection algorithm and deform contours by minimizing an energy functional that incorporates a shape term in a sparse shape prior representation, an adaptive contour occlusion penalty term, and a boundary term encouraging contours to converge to strong edges. As a result, our approach is able to deal with mutual occlusions and detect contours of multiple intersected nuclei simultaneously. Our method is applied to several whole-slide histopathologic image datasets for nuclei segmentation. The proposed method is compared with other state-of-the-art methods and demonstrates good accuracy for nuclei detection and segmentation, suggesting its promise to support biomedical image-based investigations.

Original languageEnglish (US)
Article number017502
JournalJournal of Medical Imaging
Volume6
Issue number1
DOIs
StatePublished - Jan 1 2019

Keywords

  • graph learning
  • level set
  • nuclei segmentation
  • sparse representation
  • spectral clustering

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

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