Skin Lesion Segmentation by U-Net with Adaptive Skip Connection and Structural Awareness
Tran-Dac-Thinh Phan, Soo-Hyung Kim, Hyung-Jeong Yang, Guee-Sang Lee
Abstract
Skin lesion segmentation is one of the pivotal stages in the diagnosis of melanoma. Many methods have been proposed but, to date, this is still a challenging task. Variations in size and color, the fuzzy boundary and the low contrast between lesion and normal skin are the adverse factors for deficient or excessive delineation of lesions, or even inaccurate lesion location detection. In this paper, to counter these problems, we introduce a deep learning method based on U-Net architecture, which performs three tasks, namely lesion segmentation, boundary distance map regression and contour detection. The two auxiliary tasks provide an awareness of boundary and shape to the main encoder, which improves the object localization and pixel-wise classification in the transition region from lesion tissues to healthy tissues. Moreover, concerning the large variation in size, the Selective Kernel modules, which are placed in the skip connections, transfer the multi-receptive field features from the encoder to the decoder. Our methods are evaluated on three publicly available datasets: ISBI2016, ISBI 2017 and PH2. The extensive experimental results show the effectiveness of the proposed method in the task of skin lesion segmentation.