DINOv3 with Test-Time Training for Medical Image Registration
This addresses the need for clinical adoption by reducing reliance on large training datasets, though it is incremental as it builds on existing foundation models.
The paper tackled the problem of medical image registration by proposing a training-free pipeline using a frozen DINOv3 encoder and test-time optimization, achieving a mean Dice score of 0.790 on Abdomen MR-CT and 0.769 on ACDC cardiac MRI with improved deformation metrics.
Prior medical image registration approaches, particularly learning-based methods, often require large amounts of training data, which constrains clinical adoption. To overcome this limitation, we propose a training-free pipeline that relies on a frozen DINOv3 encoder and test-time optimization of the deformation field in feature space. Across two representative benchmarks, the method is accurate and yields regular deformations. On Abdomen MR-CT, it attained the best mean Dice score (DSC) of 0.790 together with the lowest 95th percentile Hausdorff Distance (HD95) of 4.9+-5.0 and the lowest standard deviation of Log-Jacobian (SDLogJ) of 0.08+-0.02. On ACDC cardiac MRI, it improves mean DSC to 0.769 and reduces SDLogJ to 0.11 and HD95 to 4.8, a marked gain over the initial alignment. The results indicate that operating in a compact foundation feature space at test time offers a practical and general solution for clinical registration without additional training.