Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6863526 | Neurocomputing | 2018 | 55 Pages |
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.
Keywords
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Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Siqi Li, Huiyan Jiang, Yu-dong Yao, Wenbo Pang, Qingjiao Sun, Li Kuang,