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Structure convolutional extreme learning machine and case-based shape template for HCC nucleus segmentation
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DOI：10.1016/j.neucom.2018.05.013.png)
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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. (C) 2018 Elsevier B.V. All rights reserved.
Keyword:
Nucleus segmentation
Structure convolutional extreme learning machine
Case-based shape template
Boundary inference
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对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
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6.5
论文数:
2.5W
被引数:
6.5W
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