@article{harrison2022machine,
author = {Harrison, K and Pullen, H and Welsh, C and Oktay, Ozan and Alvarez-Valle, Javier and Jena, R},
title = {Machine Learning for Auto-Segmentation in Radiotherapy Planning},
year = {2022},
month = {January},
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.},
url = {http://approjects.co.za/?big=en-us/research/publication/the-royal-college-of-radiologists-clinical-oncology/},
pages = {74-88},
journal = {The Royal College of Radiologists - Clinical Oncology},
volume = {34},
number = {2},
}