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健康问题,可以通过计算机辅助诊断来减轻这一常见疾病的负担。图像中的皮肤损伤分化是实现这一目标的一个重要步骤。然而,自然和人工文物的存在(如毛发和气泡)、内在因素(如腐蚀形状和对比)以及图像采集条件的变化使皮肤损伤分化成为一项具有挑战性的任务。最近,各种研究人员探索了深层次学习模型对皮肤损伤分化的可适用性。在这次调查中,我们交叉检查了177份研究论文,其中涉及皮肤损伤的深层次基于学习的分化。我们从几个方面分析了这些作品,包括输入数据(数据集、预处理和合成数据生成)、模型设计(结构、模块和损失)和评价方面(数据注解要求和分解性表现)。我们从某些半面的角度,从系统的角度讨论了这些选择如何影响当前趋势,以及应如何解决其局限性。为了便于比较,我们从一个全面的表格中总结所有作品,作为在线交互式表格。https/comvisions。</s>