Medical image segmentation is an important step in medical image analysis, especially as a crucial prerequisite for efficient disease diagnosis and treatment. The use of deep learning for image segmentation has become a prevalent trend. The widely adopted approach currently is U-Net and its variants. Additionally, with the remarkable success of pre-trained models in natural language processing tasks, transformer-based models like TransUNet have achieved desirable performance on multiple medical image segmentation datasets. In this paper, we conduct a survey of the most representative four medical image segmentation models in recent years. We theoretically analyze the characteristics of these models and quantitatively evaluate their performance on two benchmark datasets (i.e., Tuberculosis Chest X-rays and ovarian tumors). Finally, we discuss the main challenges and future trends in medical image segmentation. Our work can assist researchers in the related field to quickly establish medical segmentation models tailored to specific regions.

翻译：医学图像分割是医学图像分析的重要步骤，尤其作为高效疾病诊断和治疗的关键前提。利用深度学习进行图像分割已成为普遍趋势。当前广泛采用的方法是U-Net及其变体。此外，随着预训练模型在自然语言处理任务中取得显著成功，基于Transformer的模型（如TransUNet）在多个医学图像分割数据集上取得了理想性能。本文对近年来最具代表性的四种医学图像分割模型进行了综述。我们从理论上分析了这些模型的特点，并在两个基准数据集（即结核病胸部X光片和卵巢肿瘤）上对其性能进行了定量评估。最后，我们讨论了医学图像分割面临的主要挑战和未来趋势。本研究可帮助相关领域的研究人员快速建立针对特定区域的医学分割模型。