Structure convolutional extreme learning machine and case-based shape template for HCC nucleus segmentation

Siqi Li, Huiyan Jiang, Yu dong Yao, Wenbo Pang, Qingjiao Sun, Li Kuang

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

Abstract

Accurate segmentation of hepatocellular carcinoma (HCC) nuclei is of great importance in automatic pathologic diagnosis. This paper proposes structure convolutional extreme learning machine (SC-ELM) and case-based shape template (CBST) methods for HCC nucleus segmentation, which could tackle complex nucleus scenarios including adhesion or overlap. First, SC-ELM is developed for global segmentation of pathology images, which is used for coarse segmentation. Then, each connected region is considered as a nucleus clump and a probability model with three energy functions is proposed for contour refinement of nucleus clumps. Finally, for complex nucleus clumps, the CBST method combined with pixel-based classification is utilized for unclear or lost boundary inference. Experimentations with 127 liver pathology images demonstrate the performance advantages of our proposed method as compared with related work.

Original languageEnglish
Pages (from-to)9-26
Number of pages18
JournalNeurocomputing
Volume312
DOIs
StatePublished - 27 Oct 2018

Keywords

  • Boundary inference
  • Case-based shape template
  • Nucleus segmentation
  • Structure convolutional extreme learning machine

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