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

• URL: http://arxiv.org/abs/2206.15255v1
• Date: Thu, 30 Jun 2022 13:06:27 GMT
• Title: Neural Rendering for Stereo 3D Reconstruction of Deformable Tissues in Robotic Surgery
• Authors: Yuehao Wang, Yonghao Long, Siu Hin Fan, Qi Dou
• Abstract summary: Reconstruction of the soft tissues in robotic surgery from endoscopic stereo videos is important for many applications. 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.
• Score: 18.150476919815382
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

Related papers

• Intraoperative Registration by Cross-Modal Inverse Neural Rendering [61.687068931599846]
  We present a novel approach for 3D/2D intraoperative registration during neurosurgery via cross-modal inverse neural rendering. Our approach separates implicit neural representation into two components, handling anatomical structure preoperatively and appearance intraoperatively. We tested our method on retrospective patients' data from clinical cases, showing that our method outperforms state-of-the-art while meeting current clinical standards for registration.
  arXiv  Detail & Related papers  (2024-09-18T13:40:59Z)
• A Review of 3D Reconstruction Techniques for Deformable Tissues in Robotic Surgery [8.909938295090827]
  NeRF-based techniques have recently garnered attention for the ability to reconstruct scenes implicitly. On the other hand, 3D-GS represents scenes explicitly using 3D Gaussians and projects them onto a 2D plane as a replacement for the complex volume rendering in NeRF. This work explores and reviews state-of-the-art (SOTA) approaches, discussing their innovations and implementation principles.
  arXiv  Detail & Related papers  (2024-08-08T12:51:23Z)
• SurgicalGaussian: Deformable 3D Gaussians for High-Fidelity Surgical Scene Reconstruction [17.126895638077574]
  Dynamic reconstruction of deformable tissues in endoscopic video is a key technology for robot-assisted surgery. NeRFs struggle to capture intricate details of objects in the scene. Our network outperforms existing method on many aspects, including rendering quality, rendering speed and GPU usage.
  arXiv  Detail & Related papers  (2024-07-06T09:31:30Z)
• Deform3DGS: Flexible Deformation for Fast Surgical Scene Reconstruction with Gaussian Splatting [20.147880388740287]
  This work presents a novel fast reconstruction framework, termed Deform3DGS, for deformable tissues during endoscopic surgery. We introduce 3D Gaussian Splatting, an emerging technology in real-time 3D rendering, into surgical scenes by integrating a point cloud. We also propose a novel flexible deformation modeling scheme (FDM) to learn tissue deformation dynamics at the level of individual Gaussians.
  arXiv  Detail & Related papers  (2024-05-28T05:14:57Z)
• Creating a Digital Twin of Spinal Surgery: A Proof of Concept [68.37190859183663]
  Surgery digitalization is the process of creating a virtual replica of real-world surgery. We present a proof of concept (PoC) for surgery digitalization that is applied to an ex-vivo spinal surgery. We employ five RGB-D cameras for dynamic 3D reconstruction of the surgeon, a high-end camera for 3D reconstruction of the anatomy, an infrared stereo camera for surgical instrument tracking, and a laser scanner for 3D reconstruction of the operating room and data fusion.
  arXiv  Detail & Related papers  (2024-03-25T13:09:40Z)
• FLex: Joint Pose and Dynamic Radiance Fields Optimization for Stereo Endoscopic Videos [79.50191812646125]
  Reconstruction of endoscopic scenes is an important asset for various medical applications, from post-surgery analysis to educational training. We adress the challenging setup of a moving endoscope within a highly dynamic environment of deforming tissue. We propose an implicit scene separation into multiple overlapping 4D neural radiance fields (NeRFs) and a progressive optimization scheme jointly optimizing for reconstruction and camera poses from scratch. This improves the ease-of-use and allows to scale reconstruction capabilities in time to process surgical videos of 5,000 frames and more; an improvement of more than ten times compared to the state of the art while being agnostic to external tracking information
  arXiv  Detail & Related papers  (2024-03-18T19:13:02Z)
• BASED: Bundle-Adjusting Surgical Endoscopic Dynamic Video Reconstruction using Neural Radiance Fields [5.773068487121897]
  Reconstruction of deformable scenes from endoscopic videos is important for many applications. Our work adopts the Neural Radiance Fields (NeRF) approach to learning 3D implicit representations of scenes. We demonstrate this approach on endoscopic surgical scenes from robotic surgery.
  arXiv  Detail & Related papers  (2023-09-27T00:20:36Z)
• Disruptive Autoencoders: Leveraging Low-level features for 3D Medical Image Pre-training [51.16994853817024]
  This work focuses on designing an effective pre-training framework for 3D radiology images. We introduce Disruptive Autoencoders, a pre-training framework that attempts to reconstruct the original image from disruptions created by a combination of local masking and low-level perturbations. The proposed pre-training framework is tested across multiple downstream tasks and achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2023-07-31T17:59:42Z)
• Neural LerPlane Representations for Fast 4D Reconstruction of Deformable Tissues [52.886545681833596]
  LerPlane is a novel method for fast and accurate reconstruction of surgical scenes under a single-viewpoint setting. LerPlane treats surgical procedures as 4D volumes and factorizes them into explicit 2D planes of static and dynamic fields. LerPlane shares static fields, significantly reducing the workload of dynamic tissue modeling.
  arXiv  Detail & Related papers  (2023-05-31T14:38:35Z)
• Neural 3D Reconstruction in the Wild [86.6264706256377]
  We introduce a new method that enables efficient and accurate surface reconstruction from Internet photo collections. We present a new benchmark and protocol for evaluating reconstruction performance on such in-the-wild scenes.
  arXiv  Detail & Related papers  (2022-05-25T17:59:53Z)
• E-DSSR: Efficient Dynamic Surgical Scene Reconstruction with Transformer-based Stereoscopic Depth Perception [15.927060244702686]
  We present an efficient reconstruction pipeline for highly dynamic surgical scenes that runs at 28 fps. Specifically, we design a transformer-based stereoscopic depth perception for efficient depth estimation. We evaluate the proposed pipeline on two datasets, the public Hamlyn Centre Endoscopic Video dataset and our in-house DaVinci robotic surgery dataset.
  arXiv  Detail & Related papers  (2021-07-01T05:57:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.