Machine Learning for Auto-Segmentation in Radiotherapy Planning. Issue 2 (February 2022)

• Record Type: Journal Article
• Authors: Harrison, K.; Pullen, H.; Welsh, C.; Oktay, O.; Alvarez-Valle, J.; Jena, R.
• Is Part Of: Clinical oncology. Volume 34:Issue 2(2022)
• Page Start: 74
• Page End: 88
• Publication Date: 2022-02
• Subjects: Auto-segmentation -- Deep learning -- Machine learning -- Radiotherapy planning; Oncology -- Periodicals; Tumors -- Periodicals; Cancer -- Treatment -- Periodicals; Radiotherapy -- Periodicals; Neoplasms -- Periodicals; Cancer -- Radiotherapy; Cancer -- Treatment; Oncology; Medical radiology; Radiotherapy; Tumors; Electronic journals; Periodicals; 616.994
• DOI: 10.1016/j.clon.2021.12.003
• Languages: English
• ISSNs: 0936-6555

Abstract

Abstract: 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 algorithms, 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 approaches 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.