CUFormer is a modality-aware registration framework that aligns intraoperative ultrasound (US) bone-surface point clouds with preoperative computed tomography (CT) models, addressing challenges from differing imaging physics and partial surface coverage. It introduces a Modality-Aware Transformer (MAT) that encodes modality through identity embeddings and uses modality-specific adapters within a shared geometric matcher, while modeling imaging visibility to weight correspondences. On two CT-US datasets (UltraBones100k and UltraBonesHip) and the CT-RGB-D SpineDepth dataset, CUFormer achieves the lowest mean registration errors, notably reducing RMSE by 65.8% on UltraBones100k compared to the best baseline.
arXiv:2608. 07564v1 Announce Type: cross Abstract: In digital dentistry and oral surgery, the registration of jawbone CT and intraoral scanner (IOS) data is essential for integrating internal bone structure with high-resolution dental surface geometry.
UBone3D is a new framework that completes 3D anatomical shapes from partial ultrasound point clouds using physics-rectified conditional flow matching. It models ultrasound artifacts with a simulated physics proxy and applies test-time physics rectification to guide the completion. The method combines a CT-trained generative shape prior and a physics consistency network, achieving higher reconstruction accuracy and anatomical fidelity than existing baselines.
arXiv:2607. 23343v1 Announce Type: cross Abstract: Intraoperative 2D/3D registration aligns preoperative CT volumes with intraoperative X-ray or fluoroscopic images and is essential for image-guided interventions.
The paper presents a clinically viable pipeline that uses routine intraoperative C‑arm fluoroscopy to derive per‑vertebra 6‑DoF poses, which are co‑registered to an RGB‑D camera via a multimodal fiducial array. This approach provides independent subsurface ground truth for validating markerless spinal tracking methods without adding radiation or altering standard surgical workflows. Ex vivo tests show an end‑to‑end accuracy of about 2 mm and 3.3°, and the framework is applied to evaluate two markerless registration strategies on real surgical data.
C3VDReg is a benchmark for local-to-local colonoscopic registration that uses the Colonoscopy 3D Video Dataset (C3VD) to generate 10,015 partial-to-partial point cloud pairs, with 2,088 held‑out test pairs. Each pair consists of a source point cloud from depth reprojection and a target point cloud from CT mesh raycasting, evaluated under a standardized protocol of 8,192 points per cloud and fixed pose conventions. Experiments show that high geometric overlap does not guarantee reliable pose recovery, revealing translation ambiguity along repetitive tubular anatomy as a key failure mode.
arXiv:2608.28715v1 Announce Type: cross Abstract: Ultrasound-guided interventions can require localization of an untracked 2D frame within a 3D anatomical reference. Rigid slice-to-volume registratio...
arXiv:2610.34600v1 Announce Type: new Abstract: Objective. Deformable image registration remains challenging because corresponding anatomy may exhibit different intensity relationships. Hand-crafted...
Objective. Deformable image registration remains challenging because corresponding anatomy may exhibit different intensity relationships. Hand-crafted modality-robust descriptors such as Modality-Inde...
The paper investigates test‑time adaptation (TTA) techniques for 3D point‑cloud registration in laparoscopic surgery, where synthetic training data must be adapted to noisy, sparse, and occluded real intraoperative reconstructions. It adapts three families of TTA methods—model, normalization, and input adaptation—to handle asymmetric shifts between preoperative meshes and intraoperative clouds, replacing classification‑based entropy objectives with correspondence‑based ones. Experiments on synthetic and real targets show that input adaptation consistently reduces registration error with low inference latency, making it the most promising approach for surgical applications.
arXiv:2609.24560v1 Announce Type: new Abstract: Confocal Laser Endomicroscopy (CLE) provides real-time, cellular-resolution optical biopsy but has a narrow field of view, which image mosaicing can ex...
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.
3D point cloud registration in laparoscopic surgery estimates the transformation between an intraoperative organ reconstructed from video and its preoperative mesh. Because ground-truth transformations are unavailable for real data, supervised networks are trained on synthetic organ pairs.