Shape Registration for Laparoscopic Images Using Offline Diffusion Learning.

Abstract

Shape registration between patient-specific organ geometries and endoscopic camera images is crucial for image-guided surgery. Although machine learning methods have been investigated for this task, collecting training datasets remains challenging because of the limited use of three-dimensional (3D) imaging in surgical settings. Offline learning with synthetic images generated from preoperative 3D-CT data has been proposed; however, ensuring robust learning under domain discrepancies between synthetic and real images remains a major challenge. In this study, we propose a diffusion-based offline learning strategy to register the shape a liver mesh in laparoscopic camera images. Within this framework, semantic organ labels serve as an image feature shared between synthetic and real-world images, thereby mitigating the domain gap and facilitating accurate registration. During model training, Gaussian noise is introduced into the registration parameters, and the visual changes in the 2D organ labels guide the training to predict the noise. Experimental results demonstrate that the prediction accuracy surpasses that of conventional approaches, producing image overlays that visualize tumors and vascular structures for intraoperative guidance.Clinical Relevance: The proposed 2D/3D registration model generates image overlays that facilitate the visualization of tumors and vascular structures in laparoscopic surgery, thereby enabling highly precise surgical guidance.