An Accessible Solution for Deformable Image Registration Compared with Learning-Based Approaches

• URL: http://arxiv.org/abs/2608.02248v1
• Date: Mon, 03 Aug 2026 13:58:25 GMT
• Title: An Accessible Solution for Deformable Image Registration Compared with Learning-Based Approaches
• Abstract summary: We show that an analytical method can still yield competitive and superior results to deep learning in a common deformable registration task.<n>Experiments on Lung250M-4B show that our proposed implementation achieves state-of-the-art results in this benchmark.
• Score: 1.5229705287183657
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Deformable image registration (DIR) is a core problem in medical image analysis; but, unlike labeling decision problems such as classification and segmentation, registration is a problem class that involves stringent physical constraints. Although deep learning methods have made faster registration possible, the resulting models are often difficult to interpret compared to hand-crafted methods with explicit objectives and interpretable physical meaning. In this work, we show that an analytical method can still yield competitive and superior results to deep learning in a common deformable registration task. We study pTVreg as a parametric total variation based registration in that context. Observing its different implementations to perform at various degrees, we introduce here an accessible implementation of this method, together with a Bayesian optimization framework that automatically sets self-parameters for any DIR task from a set of sample examples. Experiments on Lung250M-4B show that our proposed implementation achieves state-of-the-art results in this benchmark, substantially superior to existing deep learning solutions and other pTVreg variants as baselines. The source code will be made publicly available at https://github.com/oazeybekoglu/ptvreg-python .

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