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Fully convolutional networks with double-label for esophageal cancer image segmentation by self-transfer learning
Author(s): Di-Xiu Xue; Rong Zhang; Yuan-Yuan Zhao; Jian-Ming Xu; Ya-Lei Wang
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Paper Abstract

Cancer recognition is the prerequisite to determine appropriate treatment. This paper focuses on the semantic segmentation task of microvascular morphological types on narrowband images to aid clinical examination of esophageal cancer. The most challenge for semantic segmentation is incomplete-labeling. Our key insight is to build fully convolutional networks (FCNs) with double-label to make pixel-wise predictions. The roi-label indicating ROIs (region of interest) is introduced as extra constraint to guild feature learning. Trained end-to-end, the FCN model with two target jointly optimizes both segmentation of sem-label (semantic label) and segmentation of roi-label within the framework of self-transfer learning based on multi-task learning theory. The learning representation ability of shared convolutional networks for sem-label is improved with support of roi-label via achieving a better understanding of information outside the ROIs. Our best FCN model gives satisfactory segmentation result with mean IU up to 77.8% (pixel accuracy > 90%). The results show that the proposed approach is able to assist clinical diagnosis to a certain extent.

Paper Details

Date Published: 21 July 2017
PDF: 5 pages
Proc. SPIE 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017), 104202D (21 July 2017); doi: 10.1117/12.2282000
Show Author Affiliations
Di-Xiu Xue, Univ. of Science and Technology of China (China)
Key Lab. of Electromagnetic Space Information (China)
Rong Zhang, Univ. of Science and Technology of China (China)
Key Lab. of Electromagnetic Space Information (China)
Yuan-Yuan Zhao, The First Affiliated Hospital of Anhui Medical Univ. (China)
Jian-Ming Xu, The First Affiliated Hospital of Anhui Medical Univ. (China)
Ya-Lei Wang, The First Affiliated Hospital of Anhui Medical Univ. (China)


Published in SPIE Proceedings Vol. 10420:
Ninth International Conference on Digital Image Processing (ICDIP 2017)
Charles M. Falco; Xudong Jiang, Editor(s)

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