Conditional organs-of-interest segmentation with plausible inter-fraction variation simulation for pancreatic magnetic resonance-guided radiotherapy in limited-data settings

Background and Purpose: Manual contouring of organs of interest (OoIs) is a major bottleneck in pancreatic magnetic resonance-guided online adaptive radiotherapy (oART). We developed C-SegDeform, a data-efficient conditional segmentation framework that used structure-guided deformation-based augmentations to simulate plausible inter-fraction anatomical variation and leveraged organ-specific conditioning as an alternative to registration-based contour propagation (Prop-ROIs) in limited-data settings. Materials and Methods: discrepancies relative to prescribed dose), and clinical (Likert scale and post-auto-contour editing times by two oncology consultants) assessments. Results: ). Clinicians ranked 98% of C-SegDeform contours as requiring no or minor edits (vs. 70% for Prop-ROIs), with editing times under 6 min and full OoI generation completed in under 40 s. Conclusions: C-SegDeform provided a robust alternative to contour propagation, generating accurate contours rapidly with minimal edits, thereby reducing on-couch time and streamlining the workflow.