  Skin cancer is a major public health problem that could benefit from
computer-aided diagnosis to reduce the burden of this common disease. Skin
lesion segmentation from images is an important step toward achieving this
goal. However, the presence of natural and artificial artifacts (e.g., hair and
air bubbles), intrinsic factors (e.g., lesion shape and contrast), and
variations in image acquisition conditions make skin lesion segmentation a
challenging task. Recently, various researchers have explored the applicability
of deep learning models to skin lesion segmentation. In this survey, we
cross-examine 177 research papers that deal with deep learning-based
segmentation of skin lesions. We analyze these works along several dimensions,
including input data (datasets, preprocessing, and synthetic data generation),
model design (architecture, modules, and losses), and evaluation aspects (data
annotation requirements and segmentation performance). We discuss these
dimensions both from the viewpoint of select seminal works, and from a
systematic viewpoint, examining how those choices have influenced current
trends, and how their limitations should be addressed. To facilitate
comparisons, we summarize all examined works in a comprehensive table as well
as an interactive table available online at
https://github.com/sfu-mial/skin-lesion-segmentation-survey.
