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Automated Tumor Segmentation in Radiotherapy
Art der Publikation: Artikel
Zitat:
Zeitschrift: Seminars in Radiation Oncology
Band: 32
Nummer: 4
Jahr: 2022
Monat: Oktober
Seiten: 319-329
ISSN: 1053-4296
URL: https://www.sciencedirect.com/...
DOI: https://doi.org/10.1016/j.semradonc.2022.06.002
Abriss: Autosegmentation of gross tumor volumes holds promise to decrease clinical demand and to provide consistency across clinicians and institutions for radiation treatment planning. Additionally, autosegmentation can enable imaging analyses such as radiomics to construct and deploy large studies with thousands of patients. Here, we review modern results that utilize deep learning approaches to segment tumors in 5 major clinical sites: brain, head and neck, thorax, abdomen, and pelvis. We focus on approaches that inch closer to clinical adoption, highlighting winning entries in international competitions, unique network architectures, and novel ways of overcoming specific challenges. We also broadly discuss the future of gross tumor volumes autosegmentation and the remaining barriers that must be overcome before widespread replacement or augmentation of manual contouring.
Schlagworte:
Autoren Savjani, Ricky R.
Lauria, Michael
Bose, Supratik
Deng, Jie
Yuan, Ye
Andrearczyk, Vincent
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