Semi-automatic skin lesion segmentation via fully convolutional networks

Segmentation of skin lesions is considered as an important step in computer aided diagnosis (CAD) for melanoma diagnosis. There have many attempts to segment skin lesions in a semi- or fully-automated manner. Existing methods, however, have problems with over- or under-segmentation and do not perform well with challenging skin lesions such as when a lesion is partially connected to the background or when image contrast is low. To overcome these limitations, we propose a new semi-automated skin lesion segmentation method that incorporates fully convolutional networks (FCNs) with multi-scale integration. We leverage the use of FCNs to derive high-level semantic information with simple user interaction e.g., a single click to accurately segment skin lesions of various complexity. Our experiments with 379 skin lesion images show that our proposed method achieves better segmentation results when compared to the state-of-the-art skin lesion segmentation methods for challenging skin lesions.

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2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)

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978-1-5090-1172-8

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