关键词: 皮肤病变分割, 深度学习, 医学图像, 卷积神经网络

Abstract: Skin lesions exhibit a wide variety of types and complex clinical manifestations, ranging from benign conditions to malignant melanomas. Early detection and accurate segmentation of these lesions are critical for the diagnosis and treatment of skin cancer, particularly in the early identification and localization of high-risk lesions such as malignant melanoma, which can significantly improve patient survival rates. In recent years, deep learning techniques have achieved remarkable progress in skin lesion image segmentation, greatly enhancing both accuracy and efficiency. This paper presents a comprehensive review of deep learning research in the field of skin lesion image segmentation. First, various skin lesion imaging modalities and commonly used public datasets are introduced, along with a summary of standard evaluation metrics. Then, addressing the prevalent issues of noise and artifacts in images, a detailed discussion on various image preprocessing and augmentation techniques is provided. Subsequently, deep learning-based segmentation methods are elaborated, covering U-Net, Transformer, SAM, Mamba, and multi-network fusion models. The main architectural designs, advantages, limitations, and segmentation performance of these models are comparatively analyzed. Finally, the current challenges and issues in this field are examined, and future research directions are proposed, aiming to provide valuable insights for the continued development of skin lesion image segmentation.

Key words: skin lesions segmentation, deep learning, medical imaging, convolutional neural network