View article

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 …