Slice-to-volume registration between ultrasound (US) and preoperative computed tomography (CT) imaging would enhance many minimally invasive interventions, for example by locating soft tissue structur...
arXiv:2607. 17810v1 Announce Type: cross Abstract: Accurate 3D--2D liver registration, which aligns preoperative 3D models to partial, view-dependent intraoperative surface observations, is critical for AR-guided laparoscopic surgery but remains challenging due to severe occlusion, limited visibility, and the lack of 3D ground-truth supervision.
arXiv:2606. 17379v1 Announce Type: cross Abstract: Accurate intraoperative liver registration is challenging due to substantial soft-tissue deformation yet sparse intraoperative measurements.
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: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...
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.
Objective. Deformable image registration remains challenging because corresponding anatomy may exhibit different intensity relationships. Hand-crafted modality-robust descriptors such as Modality-Inde...
arXiv:2610.34600v1 Announce Type: new Abstract: Objective. Deformable image registration remains challenging because corresponding anatomy may exhibit different intensity relationships. Hand-crafted...
arXiv:2607. 13475v1 Announce Type: cross Abstract: Surgical tissue retraction requires effective manipulation planning under partial and noisy perception.
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.
ReliMorph is a novel 3D deformable registration framework that integrates correspondence reliability with spatial expert routing and residual velocity gating. By using local correlation distributions to assess match ambiguity, the method guides expert fusion and velocity updates during coarse‑to‑fine refinement, with inverse‑inconsistency feedback refining decisions at finer levels. Experiments show that ReliMorph consistently improves anatomical overlap over competing methods in both unimodal and multimodal registration tasks.