Deep learning based automatic segmentation of organs-at-risk for 0.35 T MRgRT of lung tumors

Deep learning based automatic segmentation of organs-at-risk for 0.35 T MRgRT of lung tumors

Abstract Background and purpose Magnetic resonance imaging guided radiotherapy (MRgRT) offers treatment plan adaptation to the anatomy of the day. In the current MRgRT workflow, this requires the time consuming and repetitive task of manual delineation of organs-at-risk (OARs), which is also prone t...

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Bibliographic Details
Main Authors: Marvin F. Ribeiro, Sebastian Marschner, Maria Kawula, Moritz Rabe, Stefanie Corradini, Claus Belka, Marco Riboldi, Guillaume Landry, Christopher Kurz
Format: Article
Language:English
Published: BMC 2023-08-01
Series:Radiation Oncology
Subjects:
Deep learning
Auto-segmentation
MR-Linac
MRI-guidance
Thorax
Online Access:https://doi.org/10.1186/s13014-023-02330-4

Internet

https://doi.org/10.1186/s13014-023-02330-4

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