Uncertainty-aware registration for probabilistic margin assessment in renal cell carcinoma ablation

Quantitative assessment of renal cell carcinoma (RCC) ablation margins requires reliable registration of pre- and post-ablation images. However, ablation-related tissue changes weaken local anatomical correspondence, making deformation estimates potentially non-unique. We developed an uncertainty-aware registration framework for more reliable margin estimation. Approach: An ensemble of plausible registrations was generated by stochastically sampling key registration settings, including the registration mask, similarity metric, B-spline grid spacing, and initial alignment, with Latin hypercube sampling used to improve parameter-space coverage. Registration uncertainty was characterized from this ensemble and propagated into probabilistic margin estimation. Evaluation used 25 biomechanically simulated cases with known deformation fields and 47 clinical RCC cases. Main results: In simulation, 98 ± 2% of kidney voxels had a mean displacement error below 1 mm, and uncertainty was strongly associated with registration error (median case-wise Spearman correlation, 0.99). In the clinical study, our method achieved anatomical alignment consistent with the deterministic baselines, with a median kidney Dice similarity coefficient of 0.944 and a median local mean absolute surface distance of 0.5 mm. In general, our method maintained a sensitivity of 1.00 while achieving higher positive predictive value (PPV) than the deterministic methods. Under the global criterion, PPV was 0.70 versus 0.54-0.58, although the pairwise differences between our method and each deterministic method were not statistically significant. Under the focal criterion, PPV was 0.78 versus 0.29-0.50. The pairwise differences were significant between our method and the standard and penalty-constrained B-spline methods (both p<0.001), but not between our method and the diffeomorphic method (p=0.063). Significance: The framework extends registration uncertainty estimation to RCC ablation-margin assessment by converting variability in plausible correspondences into probabilistic margin maps. It highlights negative-margin regions consistently identified across registrations, reduces registration-sensitive false-positive local tumor progression predictions, and may support more reliable postoperative evaluation. .