An Improved and Robust Encoder–Decoder for Skin Lesion Segmentation

Automatic segmentation of skin lesions is an important step in computer-aided diagnosis systems for melanoma detection. Although numerous methods have been proposed in the literature, this task is still a challenging issue due to the similarity between different lesions and complex visual characteristics that may be presented in the images. In this paper, we propose major modifications to the state-of-the-art U-Net structure to further improve its capability in skin lesion segmentation. These modifications are presented in both the encoding and the decoding paths. Instead of using only standard convolutional layers like U-Net, the proposed encoding path consists of 10 standard convolutional layers, which are inspired from the Visual Geometry Group (VGG16) network, followed by a pyramid pooling module and a dilated convolutional block. This combination enables to learn better representative feature maps and preserve more spatial resolution. Furthermore, dilated residual blocks are introduced in the decoding path to further refine the segmentation maps. The experimental results on three datasets including the IEEE International Symposium on Biomedical Imaging (ISBI) 2017, ISBI 2016, and PH2 showed that our proposed method has better performance than the basic U-Net, FCN, SegNet, and U-Net + + , and achieved the performance of state-of-the-art segmentation techniques, with minimum pre- and post-processing operations.