Fu Yabo, Lei Yang, Wang Tonghe, Curran Walter J, Liu Tian, Yang Xiaofeng
Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, USA.
Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, USA.
Phys Med. 2021 May;85:107-122. doi: 10.1016/j.ejmp.2021.05.003. Epub 2021 May 13.
Deep learning has revolutionized image processing and achieved the-state-of-art performance in many medical image segmentation tasks. Many deep learning-based methods have been published to segment different parts of the body for different medical applications. It is necessary to summarize the current state of development for deep learning in the field of medical image segmentation. In this paper, we aim to provide a comprehensive review with a focus on multi-organ image segmentation, which is crucial for radiotherapy where the tumor and organs-at-risk need to be contoured for treatment planning. We grouped the surveyed methods into two broad categories which are 'pixel-wise classification' and 'end-to-end segmentation'. Each category was divided into subgroups according to their network design. For each type, we listed the surveyed works, highlighted important contributions and identified specific challenges. Following the detailed review, we discussed the achievements, shortcomings and future potentials of each category. To enable direct comparison, we listed the performance of the surveyed works that used thoracic and head-and-neck benchmark datasets.
深度学习彻底改变了图像处理，并在许多医学图像分割任务中取得了最先进的性能。许多基于深度学习的方法已经发表，用于为不同的医学应用分割身体的不同部位。有必要总结医学图像分割领域深度学习的当前发展状况。在本文中，我们旨在提供一篇全面的综述，重点关注多器官图像分割，这对于放射治疗至关重要，因为在放射治疗中，需要勾勒出肿瘤和危及器官的轮廓以进行治疗计划。我们将调查的方法分为两大类，即“逐像素分类”和“端到端分割”。每个类别根据其网络设计进一步细分为子组。对于每种类型，我们列出了调查的作品，突出了重要贡献并确定了具体挑战。在详细综述之后，我们讨论了每个类别的成就、缺点和未来潜力。为了便于直接比较，我们列出了使用胸部和头颈部基准数据集的调查作品的性能。
