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Article Abstract
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.
Materials And Methods: Data from 151 abdominal cancer patients treated at a 0.35 T MR-Linac (151 planning and 215 fraction MRIs) were included. Population baseline models (BMs) were trained on 107 planning MRIs for one-class segmentation of the aorta, bowel, duodenum, kidneys, liver, spinal canal, and stomach. PS models were obtained by fine-tuning the BMs using the planning MRI ( ). Maximal improvement by continuously updating the PS models was investigated by adding the first four out of five fraction MRIs ( ). Similarly, PS models without BM were trained ( and ). All hyperparameters were optimized using 23 patients, and the methods were tested on the remaining 21 patients. Evaluation involved Dice similarity coefficient (DSC), average ( ) and the 95 percentile (HD) Hausdorff distance. A qualitative contour assessment by a radiation oncologist was performed for BM, , and .
Results: and networks had the best geometric performance. and BMs showed similar DSC and HDs values, however models outperformed BMs. predictions scored the best in the qualitative evaluation, followed by the BMs and models.
Conclusion: Personalized auto-segmentation models outperformed the population BMs. In most cases, delineations were judged to be directly usable for treatment adaptation without further corrections, suggesting a potential time saving during fractionated treatment.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11972049 | PMC |
| http://dx.doi.org/10.1002/mp.17580 | DOI Listing |
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