Background:
Manual contour corrections during fractionated magnetic resonance (MR)-guided radiotherapy (MRgRT) are time-consuming. Conventional population models for deep learning auto-segmentation might be suboptimal for MRgRT at MR-Linacs since they do not incorporate manual segmentation from treatment planning and previous fractions.
Purpose:
In this work, we investigate patient-specific (PS) auto-segmentation methods leveraging expert-segmented planning and prior fraction MR images (MRIs) to improve auto-segmentation on consecutive treatment days.
Conclusion:
Personalized auto-segmentation models outperformed the population BMs. In most cases, PS(BM) delineations were judged to be directly usable for treatment adaptation without further corrections, suggesting a potential time saving during fractionated treatment
