Uncertainty-Aware Annotation Protocol to Evaluate Deformable Registration Algorithms
Loic Peter, Daniel C. Alexander, Caroline Magnain, Juan Eugenio, Iglesias

TL;DR
This paper presents a new uncertainty-aware annotation protocol for deformable registration that improves gold standard creation, evaluation accuracy, and error visualization, by iteratively selecting informative points and accounting for annotation uncertainty.
Contribution
It introduces a principled, iterative framework that incorporates spatial uncertainty and informativeness in landmark annotation for deformable registration evaluation.
Findings
Effective annotation selection based on informativeness.
Enhanced evaluation of registration algorithms.
Dense error visualization from sparse annotations.
Abstract
Landmark correspondences are a widely used type of gold standard in image registration. However, the manual placement of corresponding points is subject to high inter-user variability in the chosen annotated locations and in the interpretation of visual ambiguities. In this paper, we introduce a principled strategy for the construction of a gold standard in deformable registration. Our framework: (i) iteratively suggests the most informative location to annotate next, taking into account its redundancy with previous annotations; (ii) extends traditional pointwise annotations by accounting for the spatial uncertainty of each annotation, which can either be directly specified by the user, or aggregated from pointwise annotations from multiple experts; and (iii) naturally provides a new strategy for the evaluation of deformable registration algorithms. Our approach is validated on four…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsRobotics and Sensor-Based Localization · Advanced Image and Video Retrieval Techniques · Advanced Vision and Imaging
