Reg-TTR, Test-Time Refinement for Fast, Robust and Accurate Image Registration
Lin Chen, Yue He, Fengting Zhang, Yaonan Wang, Fengming Lin, Xiang Chen, Min Liu

TL;DR
Reg-TTR is a test-time refinement framework that enhances deep learning-based image registration accuracy by combining traditional and modern methods, achieving state-of-the-art results with minimal additional inference time.
Contribution
We introduce Reg-TTR, a novel test-time refinement approach that improves registration accuracy of foundation models while maintaining fast inference speeds.
Findings
Achieves state-of-the-art registration performance.
Requires only 21% additional inference time.
Effectively narrows the gap between foundation models and specialized methods.
Abstract
Traditional image registration methods are robust but slow due to their iterative nature. While deep learning has accelerated inference, it often struggles with domain shifts. Emerging registration foundation models offer a balance of speed and robustness, yet typically cannot match the peak accuracy of specialized models trained on specific datasets. To mitigate this limitation, we propose Reg-TTR, a test-time refinement framework that synergizes the complementary strengths of both deep learning and conventional registration techniques. By refining the predictions of pre-trained models at inference, our method delivers significantly improved registration accuracy at a modest computational cost, requiring only 21% additional inference time (0.56s). We evaluate Reg-TTR on two distinct tasks and show that it achieves state-of-the-art (SOTA) performance while maintaining inference speeds…
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Taxonomy
TopicsAdvanced Neural Network Applications · Medical Image Segmentation Techniques · Advanced Image and Video Retrieval Techniques
