Neural Rendering for Stereo 3D Reconstruction of Deformable Tissues in Robotic Surgery (2206.15255v1)

Abstract: Reconstruction of the soft tissues in robotic surgery from endoscopic stereo videos is important for many applications such as intra-operative navigation and image-guided robotic surgery automation. Previous works on this task mainly rely on SLAM-based approaches, which struggle to handle complex surgical scenes. Inspired by recent progress in neural rendering, we present a novel framework for deformable tissue reconstruction from binocular captures in robotic surgery under the single-viewpoint setting. Our framework adopts dynamic neural radiance fields to represent deformable surgical scenes in MLPs and optimize shapes and deformations in a learning-based manner. In addition to non-rigid deformations, tool occlusion and poor 3D clues from a single viewpoint are also particular challenges in soft tissue reconstruction. To overcome these difficulties, we present a series of strategies of tool mask-guided ray casting, stereo depth-cueing ray marching and stereo depth-supervised optimization. With experiments on DaVinci robotic surgery videos, our method significantly outperforms the current state-of-the-art reconstruction method for handling various complex non-rigid deformations. To our best knowledge, this is the first work leveraging neural rendering for surgical scene 3D reconstruction with remarkable potential demonstrated. Code is available at: https://github.com/med-air/EndoNeRF.

• The paper presents a neural rendering framework using dynamic neural radiance fields to reconstruct 3D deformable tissues from stereo endoscopic videos while addressing occlusions.
• It introduces innovative techniques like tool mask-guided ray casting and depth-cueing ray marching to outperform traditional SLAM approaches in surgical scene reconstruction.
• Experimental results on DaVinci surgical videos show significant improvements with a 16.433 PSNR increase, 0.295 SSIM gain, and a 0.342 LPIPS reduction compared to previous methods.

Neural Rendering for Stereo 3D Reconstruction of Deformable Tissues in Robotic Surgery

The paper "Neural Rendering for Stereo 3D Reconstruction of Deformable Tissues in Robotic Surgery" introduces an innovative framework for reconstructing soft tissues in robotic surgery environments using neural rendering. This paper addresses key challenges in the surgical scene reconstruction domain, particularly those caused by tissue deformation and occlusion introduced by surgical tools. Previous work in this area has relied heavily on methods such as SLAM, which do not adequately handle complex, deformable surgical scenes.

A central innovation in this paper is its application of dynamic neural radiance fields (NeRF) in synthesizing 3D models from stereo endoscopic video data. The authors utilize multi-layer perceptrons (MLPs) to represent the dynamic aspects of surgical scenes, capturing both shape and deformation. They propose several novel strategies, including tool mask-guided ray casting and depth-cueing ray marching, which enable their model to navigate tool occlusions effectively and optimize depth supervision when limited viewpoints are available.

An experimental implementation on DaVinci robotic surgical videos demonstrates that the proposed method significantly outperforms current state-of-the-art reconstruction techniques. This includes dramatically improved performance in handling non-rigid deformations, as illustrated by the key performance metrics reported: an increase of 16.433 PSNR, 0.295 SSIM, and a decrease of 0.342 LPIPS compared to previous methods.

Methodological Innovation

The adoption of neural rendering for 3D reconstruction in this paper marks a shift from standard SLAM approaches typically used in surgical scene reconstruction. By leveraging the capabilities of NeRFs, the authors manage to present a continuous dynamic reconstruction framework capable of accommodating significant changes in tissue topology. The choice to use displacement fields in tandem with canonical radiance fields facilitates a robust modeling of complex deformations without succumbing to the instability risks of sparse warp fields, which characterized previous methodologies. The implementation of importance sampling to dynamically avoid areas obscured by tools during training further enhances reconstruction quality by prioritizing areas of interest without tool interference.

Practical and Theoretical Implications

Practically, this research introduces a new avenue for intra-operative applications such as navigation and augmented reality in robotic surgery. The neural rendering approach achieves a level of detail and accuracy that can significantly assist surgeons by providing enhanced visualizations of tissue movements and tool interactions in real time. Theoretically, the research highlights the potential of applying neural rendering techniques to dynamic, non-rigid environments beyond the field of static or rigid body scenes.

Future Directions

While the results of this paper are promising, future research may explore several potential directions. One area of interest is the enhancement of real-time processing capabilities, as achieving the latency required for interactive surgical navigation remains a challenge with current neural rendering approaches. Moreover, integrating these models with other forms of sensor data, such as force and tactile feedback, may enable more comprehensive models of the surgical environment. Expanding the dataset to include a broader variety of surgical procedures and tissue types would further assert the versatility and robustness of the proposed method.

Overall, the paper provides an insightful contribution to the field of robotic surgery, underscoring the transformative potential of neural rendering technologies in medical applications and offering a foundation for future developments in surgical scene understanding.