Gao Yuxiao, Jiang Yang, Peng Yanhong, Yuan Fujiang, Zhang Xinyue, Wang Jianfeng
College of Artificial Intelligence, Taiyuan University of Technology, Jinzhong 036000, China.
College of Mechanical Engineering, Chongqing University of Technology, Chongqing 400054, China.
Tomography. 2025 Apr 30;11(5):52. doi: 10.3390/tomography11050052.
Medical image segmentation is a critical application of computer vision in the analysis of medical images. Its primary objective is to isolate regions of interest in medical images from the background, thereby assisting clinicians in accurately identifying lesions, their sizes, locations, and their relationships with surrounding tissues. However, compared to natural images, medical images present unique challenges, such as low resolution, poor contrast, inconsistency, and scattered target regions. Furthermore, the accuracy and stability of segmentation results are subject to more stringent requirements. In recent years, with the widespread application of Convolutional Neural Networks (CNNs) in computer vision, deep learning-based methods for medical image segmentation have become a focal point of research. This paper categorizes, reviews, and summarizes the current representative methods and research status in the field of medical image segmentation. A comparative analysis of relevant experiments is presented, along with an introduction to commonly used public datasets, performance evaluation metrics, and loss functions in medical image segmentation. Finally, potential future research directions and development trends in this field are predicted and analyzed.
医学图像分割是计算机视觉在医学图像分析中的一项关键应用。其主要目标是将医学图像中的感兴趣区域与背景分离，从而帮助临床医生准确识别病变、病变大小、位置以及它们与周围组织的关系。然而，与自然图像相比，医学图像存在独特的挑战，如分辨率低、对比度差、不一致性以及目标区域分散等。此外，分割结果的准确性和稳定性受到更严格的要求。近年来，随着卷积神经网络（CNN）在计算机视觉中的广泛应用，基于深度学习的医学图像分割方法已成为研究热点。本文对医学图像分割领域当前具有代表性的方法和研究现状进行了分类、综述和总结。给出了相关实验的对比分析，同时介绍了医学图像分割中常用的公共数据集、性能评估指标和损失函数。最后，对该领域未来可能的研究方向和发展趋势进行了预测和分析。
