School of Computer Science and Engineering, University of New South Wales, Sydney, New South Wales, Australia.
Ingham Institute for Applied Medical Research and South Western Sydney Clinical School, UNSW, Liverpool, New South Wales, Australia.
J Med Imaging Radiat Oncol. 2021 Aug;65(5):578-595. doi: 10.1111/1754-9485.13286. Epub 2021 Jul 26.
Segmentation of organs and structures, as either targets or organs-at-risk, has a significant influence on the success of radiation therapy. Manual segmentation is a tedious and time-consuming task for clinicians, and inter-observer variability can affect the outcomes of radiation therapy. The recent hype over deep neural networks has added many powerful auto-segmentation methods as variations of convolutional neural networks (CNN). This paper presents a descriptive review of the literature on deep learning techniques for segmentation in radiation therapy planning. The most common CNN architecture across the four clinical sub sites considered was U-net, with the majority of deep learning segmentation articles focussed on head and neck normal tissue structures. The most common data sets were CT images from an inhouse source, along with some public data sets. N-fold cross-validation was commonly employed; however, not all work separated training, test and validation data sets. This area of research is expanding rapidly. To facilitate comparisons of proposed methods and benchmarking, consistent use of appropriate metrics and independent validation should be carefully considered.
器官和结构的分割，无论是作为目标还是危及器官，都对放射治疗的成功有重要影响。手动分割对临床医生来说是一项繁琐且耗时的任务，而且观察者间的可变性会影响放射治疗的结果。最近，深度学习网络的热潮带来了许多强大的自动分割方法，这些方法都是卷积神经网络（CNN）的变体。本文对放射治疗计划中深度学习技术的分割进行了文献综述。在所考虑的四个临床子站点中，最常见的 CNN 架构是 U-net，大多数深度学习分割文章都集中在头颈部正常组织结构上。最常见的数据集中包括来自内部来源的 CT 图像，以及一些公共数据集。通常采用 N 折交叉验证；但是，并非所有工作都将训练、测试和验证数据集分开。这一研究领域正在迅速扩展。为了便于比较提出的方法和基准测试，应仔细考虑使用一致的适当指标和独立验证。
