1. Deep learning for automatic target volume segmentation in radiation therapy: a review.
- Author
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Lin H, Xiao H, Dong L, Teo KB, Zou W, Cai J, and Li T
- Abstract
Deep learning, a new branch of machine learning algorithm, has emerged as a fast growing trend in medical imaging and become the state-of-the-art method in various clinical applications such as Radiology, Histo-pathology and Radiation Oncology. Specifically in radiation oncology, deep learning has shown its power in performing automatic segmentation tasks in radiation therapy for Organs-At-Risks (OAR), given its potential in improving the efficiency of OAR contouring and reducing the inter- and intra-observer variabilities. The similar interests were shared for target volume segmentation, an essential step of radiation therapy treatment planning, where the gross tumor volume is defined and microscopic spread is encompassed. The deep learning-based automatic segmentation method has recently been expanded into target volume automatic segmentation. In this paper, the authors summarized the major deep learning architectures of supervised learning fashion related to target volume segmentation, reviewed the mechanism of each infrastructure, surveyed the use of these models in various imaging domains (including Computational Tomography with and without contrast, Magnetic Resonant Imaging and Positron Emission Tomography) and multiple clinical sites, and compared the performance of different models using standard geometric evaluation metrics. The paper concluded with a discussion of open challenges and potential paths of future research in target volume automatic segmentation and how it may benefit the clinical practice., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://dx.doi.org/10.21037/qims-21-168). The special issue “Artificial Intelligence for Image-guided Radiation Therapy” was commissioned by the editorial office without any funding or sponsorship. LD reports NIH grants for research in proton therapy and outcome studies, unrelated to this work. Sponsored research and honoraria from Varian Medical System. TL reports consulting fees, honoraria, and travel expenses from Varian Medical Systems unrelated to this work. Patent titled “Systems and methods for automatic, customized radiation treatment plan generation for cancer” was filed. The authors have no other conflicts of interest to declare., (2021 Quantitative Imaging in Medicine and Surgery. All rights reserved.)
- Published
- 2021
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