Skin lesion segmentation by using object detection networks, DeepLab3+, and active contours

Abstract

Developing an automatic system for detection, segmentation, and classification of skin lesions is very useful to aid well-timed diagnosis of skin diseases. Lesion segmentation is a crucial task for automated diagnosis of skin cancers, as it affects significantly the accuracy of the subsequent steps. Varieties in sizes and locations of lesions, and the lesions with low-contrast boundaries make this task very challenging. In this paper, a three-stage CNN-based method is presented for accurate segmentation of lesions from dermoscopic images. At the first step, normalization, approximate locations and sizes of lesions are estimated. Due to the importance of the normalization stage, three CNN-based networks (Mask R-CNN, RetinaNet, and YOLOv3) are used for the lesion detection. A convolutional network is presented and used to combine the results of the object detection networks with a novel approach. The output of the first stage is a normalized cropped image containing the detected lesion in the center. At the second stage, segmentation, a CNN in a DeepLab3+ structure, is used to extract the lesion from the normalized image. Finally, an active contour method is used as the postprocessing to enhance the boundary of the segmented lesion. The proposed method is evaluated on well-known datasets. Experiments show that the proposed method outperforms all the previous state-of-the-art methods.