Cross-fraction prior learning for scalable organ-at-risk segmentation in abdominal MR-guided radiotherapy.

Abstract

BackgroundManual organ-at-risk (OAR) delineation takes 20-40 min per case, a major bottleneck within the 50-90 min treatment window of abdominal MR-guided adaptive radiotherapy (MRgRT). Most deep learning systems adopt single-fraction approaches that discard valuable temporal context from prior treatment fractions.PurposeThis study develops AdaptSeg, a scalable framework leveraging cross-fraction anatomical priors to substantially improve OAR segmentation without per-patient retraining.MethodsWe implemented a dual-path neural architecture conditioning current fraction segmentation on paired image-mask information from supporting fractions. AdaptSeg was instantiated with convolutional (3D UNet) and transformer-based (SwinUNETR) backbones. Evaluation used 104 pancreatic cancer patients across 520 treatment fractions for four abdominal organs (colon, duodenum, small bowel, stomach), with patient-level splitting: 72 training, 10 validation, 22 test patients. Performance metrics included Dice Similarity Coefficient (DSC), 95th percentile Hausdorff Distance (HD95), and Average Symmetric Surface Distance (ASSD); paired comparisons used two-sided Wilcoxon signed-rank tests with Benjamini-Hochberg correction, and 95% bootstrap confidence intervals for the means.ResultsCross-fraction priors improved segmentation performance for both tested backbones. The 3D UNet achieved 87.22% mean DSC versus 83.78% baseline, while SwinUNETR reached 85.19% versus 82.49% baseline. For highly deformable organs, improvements included up to 7.0 percentage point DSC gains (small bowel: 77.8% to 84.8%, pConclusionsCross-fraction anatomical priors improved OAR segmentation for both tested backbone families, indicating that temporal context is an underutilized resource in fractionated radiotherapy. AdaptSeg provides a scalable, computationally feasible framework for accelerating MRgRT workflows without per-patient adaptation, with sub-1.6 s inference compatible with the time constraints of online adaptive treatment.