Deep learning for segmentation in radiation therapy planning: a review. Issue 5 (26th July 2021)

• Record Type: Journal Article
• Authors: Samarasinghe, Gihan; Jameson, Michael; Vinod, Shalini; Field, Matthew; Dowling, Jason; Sowmya, Arcot; Holloway, Lois
• Is Part Of: Journal of medical imaging and radiation oncology. Volume 65:Issue 5(2021)
• Page Start: 578
• Page End: 595
• Publication Date: 2021-07-26
• Subjects: contouring -- deep learning -- radiation therapy -- segmentation; Radiology, Medical -- Periodicals; Radiology, Medical -- Australasia -- Periodicals; 616.0757
• DOI: 10.1111/1754-9485.13286
• Languages: English
• ISSNs: 1754-9477

Abstract

Summary: 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.