期刊
IEEE ACCESS
卷 7, 期 -, 页码 140936-140945出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2019.2943628
关键词
Lesions; Image segmentation; Feature extraction; Skin; Hair; Melanoma; Skin cancer; dermoscopy image; deep supervised learning; multi-scale feature; conditional random field
资金
- General Program of National Natural Science Foundation of China (NSFC) [61806147]
- Shanghai Natural Science Foundation of China [18ZR1441200]
- Fundamental Research Funds for the Central Universities [22120180012]
- NSFC [81571347, 61572362]
The automatic segmentation of the skin lesion on dermoscopy images is an important step for diagnosing the melanoma. However, the skin lesion segmentation is still a challenging task due to the blur lesion border, low contrast between the skin cancer region and normal tissue background, and various sizes of cancer regions. In this paper, we propose a deep supervised multi-scale network (DSM-Network), which achieves satisfied skin cancer segmentation result by utilizing the side-output layers of the network to aggregate information from shallowdeep layers, and designing a multi-scale connection block to handle a variety of cancer sizes changes. Moreover, a post-processing of the contour refinement strategy is adopted by a conditional random field (CRF) model to further improve the segmentation results. Extensive experiments on two public datasets: ISBI 2017 and PH2 have demonstrated that our designed DSM-Network has gained competitive performance compared with other state-of-the-art methods.
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