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CONReg: Uncertainty-Aware Medical Image Registration Using Conformal Prediction
Benyamin Gheiji1, Danial Elyassirad1, Mahsa Vatanparast1
1Student Research Committee, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
Journal of Imaging Informatics in Medicine
|March 12, 2026
Summary
CONReg enhances deep learning medical image registration by quantifying uncertainty. This framework improves reliability and interpretability, ensuring trustworthy predictions for clinical applications.
Area of Science:
- Medical Image Analysis
- Machine Learning
- Radiology
Background:
- Deep learning (DL) models for medical image registration lack uncertainty quantification, limiting clinical trust.
- Uncertainty quantification (UQ) is crucial for identifying unreliable predictions in medical imaging.
Purpose of the Study:
- To introduce CONReg, a novel framework for voxelwise and case-level UQ in medical image registration.
- To improve the reliability and interpretability of DL-based registration through statistically principled uncertainty estimation.
Main Methods:
- Integrated quantile regression with conformal prediction (CP) to generate predictive intervals for dense displacement fields (DDFs).
- Utilized a 3D U-Net for predicting DDFs and their quantile bounds on brain and lung datasets.
- Developed uncertainty bounding boxes (UBBxs) at keypoints and stratified cases into certain/uncertain groups.
Main Results:
- Achieved empirical coverage between 0.92-0.98, demonstrating reliable UQ.
- Certain keypoints and cases showed significantly lower target registration error (p < 0.05).
- Certain cases exhibited significantly lower mean squared error (MSE) compared to uncertain cases (p < 0.05).
Conclusions:
- CONReg provides a statistically sound method for assessing registration uncertainty.
- The framework enhances the trustworthiness and interpretability of DL models in medical image registration.
- CONReg facilitates better clinical decision-making by highlighting areas of potential prediction unreliability.
