arXiv Computer Vision
SAMReg: SAM-enabled Image Registration with ROI-based Correspondence
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arXiv Computer Vision
Sep 18
UniReg: Conditional Unified Model for Medical Image Registration
UniReg is a conditional unified model for medical image registration that adapts deformation field estimation based on anatomical priors, registration type constraints, and instance-specific features. It combines the precision of task‑specific learning with the generalization of traditional optimization, enabling effective alignment across diverse CT and MR scenarios within a single framework. Experiments show UniReg outperforms state‑of‑the‑art learning‑based methods in accuracy while providing strong cross‑scenario generalization and reducing training cost and model redundancy.
arXiv Computer Vision
Sep 24
Recursive Uncertainty-Gated Image Registration for Learning-based Algorithms
The paper introduces Recursive Uncertainty-Gated Image Registration (RUGI), an iterative refinement method that updates deformation fields predicted by learning-based registration models using a gating map. Two gating strategies are explored: an uncertainty-based approach and an image residual error approach, both concentrating updates on difficult regions. Experiments on cardiac MRI and echocardiography datasets show that RUGI consistently improves registration accuracy, with the error-gated variant reducing MSE by 27‑37% on pretrained models and lowering ejection fraction estimation errors.
arXiv Computer Vision
Sep 25
When Misalignment Becomes Supervision: Structured Label Noise in Supervised Synthetic CT Generation
The paper examines how residual misalignments from registration procedures introduce structured label noise in supervised synthetic CT (sCT) generation. It shows that voxel‑wise metrics are heavily influenced by the consistency between training and evaluation registrations, and that training with anatomically consistent registrations reduces variability and improves robustness. Introducing a perceptual loss based on a pretrained Segment Anything encoder yields sharper, more anatomically coherent sCT and highlights the need for anatomy‑oriented evaluation.
arXiv AI
Oct 19
Really Need Recursion? Revisiting One-pass Registration with Multi-level Multi-space Contextual Guidance
The paper introduces M2CG, a one-pass deformable image registration network that replaces recursive refinement with multi-level, multi-space contextual guidance. M2CG performs progressive pyramid registration within a single iteration, leveraging cues from previous, current, and subsequent steps across image, image feature, segmentation, and segmentation feature spaces. It achieves state‑of‑the‑art results on eight public medical datasets in both unsupervised and semi‑supervised settings, challenging the prevailing reliance on recursive architectures.
arXiv Computer Vision
Oct 27
FlexiMIND: Combined Pretrained Vision Features and Local Structure for Training-Free Deformable Medical Image Registration
arXiv:2610.34600v1 Announce Type: new Abstract: Objective. Deformable image registration remains challenging because corresponding anatomy may exhibit different intensity relationships. Hand-crafted...
Hugging Face Trending Papers
Oct 25
FlexiMIND: Combined Pretrained Vision Features and Local Structure for Training-Free Deformable Medical Image Registration
Objective. Deformable image registration remains challenging because corresponding anatomy may exhibit different intensity relationships. Hand-crafted modality-robust descriptors such as Modality-Inde...