Dynamic Reconstruction of Deformable Soft-tissue with Stereo Scope in Minimal Invasive Surgery
This addresses the challenge of visualizing tissue deformation for surgeons during minimally invasive procedures, though it appears incremental as it builds on existing SLAM and deformation techniques.
The paper tackles the problem of reconstructing deformable soft-tissue surfaces in minimally invasive surgery by proposing a novel SLAM algorithm that uses stereo images to incrementally build 3D models with accurate texture, demonstrating its potential in clinical applications through in-vivo experiments.
In minimal invasive surgery, it is important to rebuild and visualize the latest deformed shape of soft-tissue surfaces to mitigate tissue damages. This paper proposes an innovative Simultaneous Localization and Mapping (SLAM) algorithm for deformable dense reconstruction of surfaces using a sequence of images from a stereoscope. We introduce a warping field based on the Embedded Deformation (ED) nodes with 3D shapes recovered from consecutive pairs of stereo images. The warping field is estimated by deforming the last updated model to the current live model. Our SLAM system can: (1) Incrementally build a live model by progressively fusing new observations with vivid accurate texture. (2) Estimate the deformed shape of unobserved region with the principle As-Rigid-As-Possible. (3) Show the consecutive shape of models. (4) Estimate the current relative pose between the soft-tissue and the scope. In-vivo experiments with publicly available datasets demonstrate that the 3D models can be incrementally built for different soft-tissues with different deformations from sequences of stereo images obtained by laparoscopes. Results show the potential clinical application of our SLAM system for providing surgeon useful shape and texture information in minimal invasive surgery.