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: Forty balanced 3DVane images from 12 patients were manually contoured and pre-processed, including duodenum, both kidneys, liver, large and small bowel, spinal canal, spleen, and stomach. The training dataset (26 images) was augmented by simulating plausible session images via structure-guided deformations. Data was arranged for conditional segmentation and leave-one-out cross-validation using the nnU-Net framework. Analysis included geometric (DSC, average surface distance (ASD), and 95th percentile of Hausdorff distance (HD Results: C-SegDeform (DSC: 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.