Automatic Lesion Boundary Segmentation in Dermoscopic Images with Ensemble Deep Learning Methods

Early detection of skin cancer, particularly melanoma, is crucial to enable advanced treatment. Due to the rapid growth of skin cancers, there is a growing need of computerized analysis for skin lesions. These processes including detection, classification, and segmentation. The state-of-the-art public available datasets for skin lesions are often accompanied with a very limited amount of segmentation ground truth labeling as it is laborious and expensive. The lesion boundary segmentation is vital to locate the lesion accurately in dermoscopic images and lesion diagnosis of different skin lesion types. In this work, we propose the use of fully automated deep learning ensemble methods for accurate lesion boundary segmentation in dermoscopic images. We trained the Mask-RCNN and DeeplabV3+ methods on ISIC-2017 segmentation training dataset and evaluate the various ensemble performance of both networks on ISIC-2017 testing set, PH2 dataset. Our results showed that the proposed ensemble method segmented the skin lesions with Jaccard index of 79.58% for the ISBI 2017 test dataset. In comparison to FrCN, FCN, U-Net, and SegNet, the proposed ensemble method outperformed them by 2.48%, 7.42%, 17.95%, and 9.96% for the Jaccard index, respectively. Furthermore, the proposed ensemble method achieved a segmentation accuracy of 95.6% for some representative clinical benign cases, 90.78\% for the melanoma cases, and 91.29% for the seborrheic keratosis cases in the ISBI 2017 test dataset, exhibiting better performance than those of FrCN, FCN, U-Net, and SegNet.