Machine Learning for Auto-Segmentation in Radiotherapy Planning

Manual segmentation of target structures and organs at risk is a crucial step in the radiotherapy workflow. It has the disadvantages that it can require several hours of clinician time per patient and is prone to inter-and intra-observer variability. Automatic segmentation (auto-segmentation), using computer algo-rithms, seeks to address these issues. Advances in machine learning and computer vision have led to the development of methods for accurate and efficient auto-segmentation. This review surveys auto-segmentation techniques and applications in radiotherapy planning. It provides an overview of traditional ap-proaches to auto-segmentation, including intensity analysis, shape modelling and atlas-based methods. The focus, though, is on uses of machine learning and deep learning, including convolutional neural networks. Finally, the future of machine-learning-driven auto-segmentation in clinical settings is considered, and the barriers that must be overcome for it to be widely accepted into routine practice are highlighted. (c) 2021 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.

Automated summary

This review examines the techniques and applications of automatic segmentation in radiotherapy planning. It provides an overview of traditional methods and explores the use of machine learning and deep learning, particularly convolutional neural networks. The future of machine-learning-driven automatic segmentation in clinical settings is discussed.