arXiv:2606. 17379v1 Announce Type: cross Abstract: Accurate intraoperative liver registration is challenging due to substantial soft-tissue deformation yet sparse intraoperative measurements.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.
arXiv:2608.24422v1 Announce Type: new Abstract: Recovering the full 3D spine anatomy from intraoperative ultrasound is an ill-posed inverse problem, as the complete structure must be inferred from in...
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.
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.
arXiv:2607. 13475v1 Announce Type: cross Abstract: Surgical tissue retraction requires effective manipulation planning under partial and noisy perception.
The paper presents a method for accurately simulating soft tissue deformation and predicting forces across varying material stiffnesses and geometries. It calibrates hyperelastic constitutive models in the SOFA Framework using gravity‑loaded silicone beams, then trains a softness‑conditioned equivariant graph neural network on the calibrated simulations. The resulting model achieves sub‑millimeter deformation accuracy with 0.010 s inference time, and demonstrates that force prediction quality depends on consistent upstream calibration.
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.