Non-rigid image-volume registration for human livers in laparoscopic surgery.

ABSTRACT:

Background

The fusion of intraoperative 2D laparoscopic images with preoperative 3D scans offers significant advantages in minimally invasive surgery, such as improved spatial understanding and enhanced navigation. This study aims to enable augmented reality for deformable organs through accurate 2D-3D registration. However, achieving real-time and precise alignment remains a major challenge due to organ deformation, occlusion, and the difficulty of estimating camera parameters from monocular images.

Methods

We introduce a non-rigid image-volume registration (NRIVR) framework designed specifically for deformable human organs. Our approach employs a long short-term memory-based camera estimation neural network (LCENN) to predict camera poses directly from 2D anatomical contou

SUBMITTER: Cao Z 

PROVIDER: S-EPMC12397628 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature