Test-time Uncertainty Estimation for Medical Image Registration via Transformation Equivariance

• URL: http://arxiv.org/abs/2509.23355v1
• Date: Sat, 27 Sep 2025 15:03:06 GMT
• Title: Test-time Uncertainty Estimation for Medical Image Registration via Transformation Equivariance
• Authors: Lin Tian, Xiaoling Hu, Juan Eugenio Iglesias,
• Abstract summary: Current deep registration networks provide limited indications of whether and when their predictions are reliable.<n>We propose a test-time uncertainty estimation framework that is compatible with any pretrained networks.<n>Our framework turns any pretrained registration network into a risk-aware tool at test time, placing medical image registration one step closer to safe deployment.
• Score: 11.57571124470059
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Accurate image registration is essential for downstream applications, yet current deep registration networks provide limited indications of whether and when their predictions are reliable. Existing uncertainty estimation strategies, such as Bayesian methods, ensembles, or MC dropout, require architectural changes or retraining, limiting their applicability to pretrained registration networks. Instead, we propose a test-time uncertainty estimation framework that is compatible with any pretrained networks. Our framework is grounded in the transformation equivariance property of registration, which states that the true mapping between two images should remain consistent under spatial perturbations of the input. By analyzing the variance of network predictions under such perturbations, we derive a theoretical decomposition of perturbation-based uncertainty in registration. This decomposition separates into two terms: (i) an intrinsic spread, reflecting epistemic noise, and (ii) a bias jitter, capturing how systematic error drifts under perturbations. Across four anatomical structures (brain, cardiac, abdominal, and lung) and multiple registration models (uniGradICON, SynthMorph), the uncertainty maps correlate consistently with registration errors and highlight regions requiring caution. Our framework turns any pretrained registration network into a risk-aware tool at test time, placing medical image registration one step closer to safe deployment in clinical and large-scale research settings.

Related papers

• Towards Distribution-Shift Uncertainty Estimation for Inverse Problems with Generative Priors [4.085626952545055]
  Gene models have shown strong potential as data-driven priors for solving inverse problems.<n>We propose an instance-level, calibration-free uncertainty indicator that is sensitive to distribution shift.<n>Our proposed indicator is efficiently computable for any computational imaging inverse problem.
  arXiv  Detail & Related papers  (2025-10-13T02:58:26Z)
• Revisiting Multivariate Time Series Forecasting with Missing Values [65.30332997607141]
  Missing values are common in real-world time series.<n>Current approaches have developed an imputation-then-prediction framework that uses imputation modules to fill in missing values, followed by forecasting on the imputed data.<n>This framework overlooks a critical issue: there is no ground truth for the missing values, making the imputation process susceptible to errors that can degrade prediction accuracy.<n>We introduce Consistency-Regularized Information Bottleneck (CRIB), a novel framework built on the Information Bottleneck principle.
  arXiv  Detail & Related papers  (2025-09-27T20:57:48Z)
• Uncertainty Awareness on Unsupervised Domain Adaptation for Time Series Data [49.36938105983916]
  Unsupervised domain adaptation methods seek to generalize effectively on unlabeled test data.<n>We propose incorporating multi-scale feature extraction and uncertainty estimation to improve the model's generalization and robustness across domains.
  arXiv  Detail & Related papers  (2025-08-26T03:13:08Z)
• From Registration Uncertainty to Segmentation Uncertainty [11.294691606431526]
  We propose a novel framework to concurrently estimate both the epistemic and aleatoric segmentation uncertainties for image registration. By introducing segmentation uncertainty along with existing methods for estimating registration uncertainty, we offer vital insights into the potential uncertainties at different stages of image registration.
  arXiv  Detail & Related papers  (2024-03-08T07:16:14Z)
• Semi-weakly-supervised neural network training for medical image registration [18.520388065729552]
  This paper describes a semi-weakly-supervised registration pipeline that improves the model performance. We examine two types of augmentation methods by perturbation on network weights and image resampling. Experiments using 589 male pelvic MR images, labelled with eight anatomical ROIs, show the improvement in registration performance.
  arXiv  Detail & Related papers  (2024-02-16T14:44:40Z)
• Adaptive Correspondence Scoring for Unsupervised Medical Image Registration [9.294341405888158]
  Existing methods rely on image reconstruction as the primary supervision signal. We propose an adaptive framework that re-weights the error residuals with a correspondence scoring map during training. Our framework consistently outperforms other methods both quantitatively and qualitatively.
  arXiv  Detail & Related papers  (2023-12-01T01:11:22Z)
• Heteroscedastic Uncertainty Estimation Framework for Unsupervised Registration [32.081258147692395]
  We propose a framework for heteroscedastic image uncertainty estimation. It can adaptively reduce the influence of regions with high uncertainty during unsupervised registration. Our method consistently outperforms baselines and produces sensible uncertainty estimates.
  arXiv  Detail & Related papers  (2023-12-01T01:03:06Z)
• Joint segmentation and discontinuity-preserving deformable registration: Application to cardiac cine-MR images [74.99415008543276]
  Most deep learning-based registration methods assume that the deformation fields are smooth and continuous everywhere in the image domain. We propose a novel discontinuity-preserving image registration method to tackle this challenge, which ensures globally discontinuous and locally smooth deformation fields. A co-attention block is proposed in the segmentation component of the network to learn the structural correlations in the input images. We evaluate our method on the task of intra-subject-temporal image registration using large-scale cinematic cardiac magnetic resonance image sequences.
  arXiv  Detail & Related papers  (2022-11-24T23:45:01Z)
• Self-Supervised Training with Autoencoders for Visual Anomaly Detection [61.62861063776813]
  We focus on a specific use case in anomaly detection where the distribution of normal samples is supported by a lower-dimensional manifold. We adapt a self-supervised learning regime that exploits discriminative information during training but focuses on the submanifold of normal examples. We achieve a new state-of-the-art result on the MVTec AD dataset -- a challenging benchmark for visual anomaly detection in the manufacturing domain.
  arXiv  Detail & Related papers  (2022-06-23T14:16:30Z)
• Trustworthy Medical Segmentation with Uncertainty Estimation [0.7829352305480285]
  This paper introduces a new Bayesian deep learning framework for uncertainty quantification in segmentation neural networks. We evaluate the proposed framework on medical image segmentation data from Magnetic Resonances Imaging and Computed Tomography scans. Our experiments on multiple benchmark datasets demonstrate that the proposed framework is more robust to noise and adversarial attacks as compared to state-of-the-art segmentation models.
  arXiv  Detail & Related papers  (2021-11-10T22:46:05Z)
• A Deep Discontinuity-Preserving Image Registration Network [73.03885837923599]
  Most deep learning-based registration methods assume that the desired deformation fields are globally smooth and continuous. We propose a weakly-supervised Deep Discontinuity-preserving Image Registration network (DDIR) to obtain better registration performance and realistic deformation fields. We demonstrate that our method achieves significant improvements in registration accuracy and predicts more realistic deformations, in registration experiments on cardiac magnetic resonance (MR) images.
  arXiv  Detail & Related papers  (2021-07-09T13:35:59Z)
• Trust but Verify: Assigning Prediction Credibility by Counterfactual Constrained Learning [123.3472310767721]
  Prediction credibility measures are fundamental in statistics and machine learning. These measures should account for the wide variety of models used in practice. The framework developed in this work expresses the credibility as a risk-fit trade-off.
  arXiv  Detail & Related papers  (2020-11-24T19:52:38Z)
• Collaborative Boundary-aware Context Encoding Networks for Error Map Prediction [65.44752447868626]
  We propose collaborative boundaryaware context encoding networks called AEP-Net for error prediction task. Specifically, we propose a collaborative feature transformation branch for better feature fusion between images and masks, and precise localization of error regions. The AEP-Net achieves an average DSC of 0.8358, 0.8164 for error prediction task, and shows a high Pearson correlation coefficient of 0.9873.
  arXiv  Detail & Related papers  (2020-06-25T12:42:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.