Evaluation of artificial intelligence-based autosegmentation for a high-performance cone-beam computed tomography imaging system in the pelvic region

Evaluation of artificial intelligence-based autosegmentation for a high-performance cone-beam computed tomography imaging system in the pelvic region

Background and purpose: A novel ring-gantry cone-beam computed tomography (CBCT) imaging system shows improved image quality compared to its conventional version, but its effect on autosegmentation is unknown. This study evaluates the impact of this high-performance CBCT on autosegmentation performa...

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Bibliographic Details
Main Authors: Judith H. Sluijter, Agustinus J.A.J. van de Schoot, Abdelmounaim el Yaakoubi, Maartje de Jong, Martine S. van der Knaap - van Dongen, Britt Kunnen, Nienke D. Sijtsema, Joan J. Penninkhof, Kim C. de Vries, Steven F. Petit, Maarten L.P. Dirkx
Format: Article
Language:English
Published: Elsevier 2025-01-01
Series:Physics and Imaging in Radiation Oncology
Subjects:
AI-based autosegmentation
CBCT-guided online adaptive radiotherapy
Pelvic region
Random effects model
Online Access:http://www.sciencedirect.com/science/article/pii/S240563162400157X
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