Segmentation Regularized Training for Multi-Domain Deep Learning Registration applied to MR-Guided Prostate Cancer Radiotherapy
Journal:
arXiv
Published Date:
Jul 9, 2025
Abstract
Background: Accurate deformable image registration (DIR) is required for
contour propagation and dose accumulation in MR-guided adaptive radiotherapy
(MRgART). This study trained and evaluated a deep learning DIR method for
domain invariant MR-MR registration. Methods: A progressively refined
registration and segmentation (ProRSeg) method was trained with 262 pairs of 3T
MR simulation scans from prostate cancer patients using weighted segmentation
consistency loss. ProRSeg was tested on same- (58 pairs), cross- (72 1.5T MR
Linac pairs), and mixed-domain (42 MRSim-MRL pairs) datasets for contour
propagation accuracy of clinical target volume (CTV), bladder, and rectum. Dose
accumulation was performed for 42 patients undergoing 5-fraction MRgART.
Results: ProRSeg demonstrated generalization for bladder with similar Dice
Similarity Coefficients across domains (0.88, 0.87, 0.86). For rectum and CTV,
performance was domain-dependent with higher accuracy on cross-domain MRL
dataset (DSCs 0.89) versus same-domain data. The model's strong cross-domain
performance prompted us to study the feasibility of using it for dose
accumulation. Dose accumulation showed 83.3% of patients met CTV coverage (D95
>= 40.0 Gy) and bladder sparing (D50 <= 20.0 Gy) constraints. All patients
achieved minimum mean target dose (>40.4 Gy), but only 9.5% remained under
upper limit (<42.0 Gy). Conclusions: ProRSeg showed reasonable multi-domain
MR-MR registration performance for prostate cancer patients with preliminary
feasibility for evaluating treatment compliance to clinical constraints.
