  Automatic skin lesion segmentation on dermoscopic images is an essential step
in computer-aided diagnosis of melanoma. However, this task is challenging due
to significant variations of lesion appearances across different patients. This
challenge is further exacerbated when dealing with a large amount of image
data. In this paper, we extended our previous work by developing a deeper
network architecture with smaller kernels to enhance its discriminant capacity.
In addition, we explicitly included color information from multiple color
spaces to facilitate network training and thus to further improve the
segmentation performance. We extensively evaluated our method on the ISBI 2017
skin lesion segmentation challenge. By training with the 2000 challenge
training images, our method achieved an average Jaccard Index (JA) of 0.765 on
the 600 challenge testing images, which ranked itself in the first place in the
challenge
