Abstract:

Medical image segmentation is an important application area of computer vision in the medical image processing, its goal is to segment the target area from medical images and provide effective help for subsequent diagnosis and treatment of diseases. Since deep learning technology has made great progress in the image processing, medical image segmentation algorithm based on deep learning has gradually become the focus and hotspot of research in this field. This paper gives a description on the tasks and difficulties of medical image segmentation. Then, it details the deep learning-based medical image segmentation algorithm, classifies and summarizes the current representative methods. Moreover, this paper presents the frequently-used algorithm evaluation indicators and datasets in the field of medical image segmentation. The development of medical image segmentation technology is summarized and forecasted.

Key words: deep learning, computer vision, medical image, image segmentation

摘要：

医学影像分割是计算机视觉在医学影像处理中的一个重要应用领域，其目标是从医学影像中分割出目标区域，为后续的疾病诊断和治疗提供有效的帮助。近年来深度学习技术在图像处理方面取得了巨大进展，基于深度学习的医学影像分割算法逐渐成为该领域研究的重点和热点。叙述了计算机视觉下的医学影像分割任务及其难点，重点综述了基于深度学习的医学影像分割算法，对当前具有代表性的相关方法进行了分类和总结，介绍了医学影像分割算法常用的评价指标和数据集。对该技术的发展进行了总结和展望。

关键词: 深度学习, 计算机视觉, 医学影像, 图像分割