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，计算机辅助诊断有望减轻这一常见疾病所带来的负担。从图像中分割皮肤病变区域是实现这一目标的重要步骤。然而，自然和人工伪影（如毛发和气泡）、内在因素（如病变形状和对比度）以及图像采集条件的变化，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。本篇综述交叉审视了177篇关于基于深度学习的皮肤病变分割的研究论文。我们沿着多个维度对这些工作进行分析，包括输入数据（数据集、预处理和合成数据生成）、模型设计（架构、模块和损失函数）以及评估方面（数据标注需求和分割性能）。我们既从代表性开创性工作的视角，也从系统性视角讨论这些维度，审视这些选择如何影响了当前趋势，以及应如何解决其局限性。为便于比较，我们将所有审查的研究汇总于一个综合表格以及一个可在线访问的交互式表格中，网址为：https://github.com/sfu-mial/skin-lesion-segmentation-survey。