Medical Image Analysis Lab, School of Computing Science, Simon Fraser University, Burnaby V5A 1S6, Canada.
RECOD.ai Lab, Institute of Computing, University of Campinas, Av. Albert Einstein 1251, Campinas 13083-852, Brazil.
Med Image Anal. 2023 Aug;88:102863. doi: 10.1016/j.media.2023.102863. Epub 2023 Jun 9.
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.
皮肤癌是一个重大的公共卫生问题，可以受益于计算机辅助诊断，以减轻这种常见疾病的负担。从图像中分割皮肤病变是实现这一目标的重要步骤。然而，自然和人为伪影（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。最近，许多研究人员已经探索了深度学习模型在皮肤病变分割中的适用性。在这项调查中，我们仔细检查了 177 篇处理基于深度学习的皮肤病变分割的研究论文。我们沿着多个维度分析这些工作，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）以及评估方面（数据注释要求和分割性能）。我们从选择的开创性工作的角度，以及从系统的角度来讨论这些方面，探讨了这些选择如何影响当前的趋势，以及如何解决它们的局限性。为了便于比较，我们将所有检查的工作总结在一个全面的表格中，并在网上提供一个交互式表格。
