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.

翻译：皮肤癌是一个重大的公共卫生问题，计算机辅助诊断有助于减轻这一常见疾病的负担。从图像中分割皮肤病变区域是实现该目标的重要步骤。然而，自然与人造伪影（如毛发和气泡）、内在因素（如病变形状与对比度）以及图像采集条件的差异，使得皮肤病变分割成为一项具有挑战性的任务。近年来，众多研究者探索了深度学习模型在皮肤病变分割中的适用性。本综述系统梳理了177篇关于基于深度学习的皮肤病变分割的研究论文。我们从输入数据（数据集、预处理及合成数据生成）、模型设计（架构、模块及损失函数）以及评估维度（数据标注需求与分割性能）等多个角度对这些工作进行了分析。既从代表性开创性工作的视角，也从系统性的角度探讨了这些维度如何影响当前研究趋势及其局限性应如何解决。为便于比较，我们以综合表格形式汇总所有研究工作，并提供了在线交互式表格（访问地址：https://github.com/sfu-mial/skin-lesion-segmentation-survey）。