Validation of a deep-learning segmentation model for adult and pediatric head and neck radiotherapy in different patient positions

Validation of a deep-learning segmentation model for adult and pediatric head and neck radiotherapy in different patient positions

Background and purpose: Autocontouring for radiotherapy has the potential to significantly save time and reduce interobserver variability. We aimed to assess the performance of a commercial autocontouring model for head and neck (H&N) patients in eight orientations relevant to particle therapy w...

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Bibliographic Details
Main Authors: Linda Chen, Patricia Platzer, Christian Reschl, Mansure Schafasand, Ankita Nachankar, Christoph Lukas Hajdusich, Peter Kuess, Markus Stock, Steven Habraken, Antonio Carlino
Format: Article
Language:English
Published: Elsevier 2024-01-01
Series:Physics and Imaging in Radiation Oncology
Subjects:
Autocontouring
Radiation therapy
Artificial Intelligence
Head and neck cancer
Auto-segmentation
Organs-at-risk
Online Access:http://www.sciencedirect.com/science/article/pii/S2405631623001185

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http://www.sciencedirect.com/science/article/pii/S2405631623001185

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