Depth Over RGB: Automatic Evaluation of Open Surgery Skills Using Depth Camera

• URL: http://arxiv.org/abs/2401.10037v1
• Date: Thu, 18 Jan 2024 15:00:28 GMT
• Title: Depth Over RGB: Automatic Evaluation of Open Surgery Skills Using Depth Camera
• Authors: Ido Zuckerman, Nicole Werner, Jonathan Kouchly, Emma Huston, Shannon DiMarco, Paul DiMusto, Shlomi Laufer
• Abstract summary: This work is intended to show that depth cameras achieve similar results to RGB cameras. depth cameras offer advantages such as robustness to lighting variations, camera positioning, simplified data compression, and enhanced privacy.
• Score: 0.8246494848934447
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Purpose: In this paper, we present a novel approach to the automatic evaluation of open surgery skills using depth cameras. This work is intended to show that depth cameras achieve similar results to RGB cameras, which is the common method in the automatic evaluation of open surgery skills. Moreover, depth cameras offer advantages such as robustness to lighting variations, camera positioning, simplified data compression, and enhanced privacy, making them a promising alternative to RGB cameras. Methods: Experts and novice surgeons completed two simulators of open suturing. We focused on hand and tool detection, and action segmentation in suturing procedures. YOLOv8 was used for tool detection in RGB and depth videos. Furthermore, UVAST and MSTCN++ were used for action segmentation. Our study includes the collection and annotation of a dataset recorded with Azure Kinect. Results: We demonstrated that using depth cameras in object detection and action segmentation achieves comparable results to RGB cameras. Furthermore, we analyzed 3D hand path length, revealing significant differences between experts and novice surgeons, emphasizing the potential of depth cameras in capturing surgical skills. We also investigated the influence of camera angles on measurement accuracy, highlighting the advantages of 3D cameras in providing a more accurate representation of hand movements. Conclusion: Our research contributes to advancing the field of surgical skill assessment by leveraging depth cameras for more reliable and privacy evaluations. The findings suggest that depth cameras can be valuable in assessing surgical skills and provide a foundation for future research in this area.

Related papers

• Depth-based Privileged Information for Boosting 3D Human Pose Estimation on RGB [48.31210455404533]
  Heatmap-based 3D pose estimator is able to hallucinate depth information from the RGB frames given at inference time. depth information is used exclusively during training by enforcing our RGB-based hallucination network to learn similar features to a backbone pre-trained only on depth data.
  arXiv  Detail & Related papers  (2024-09-17T11:59:34Z)
• 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)
• SDGE: Stereo Guided Depth Estimation for 360$^\circ$ Camera Sets [65.64958606221069]
  Multi-camera systems are often used in autonomous driving to achieve a 360$circ$ perception. These 360$circ$ camera sets often have limited or low-quality overlap regions, making multi-view stereo methods infeasible for the entire image. We propose the Stereo Guided Depth Estimation (SGDE) method, which enhances depth estimation of the full image by explicitly utilizing multi-view stereo results on the overlap.
  arXiv  Detail & Related papers  (2024-02-19T02:41:37Z)
• WS-SfMLearner: Self-supervised Monocular Depth and Ego-motion Estimation on Surgical Videos with Unknown Camera Parameters [0.0]
  Building an accurate and robust self-supervised depth and camera ego-motion estimation system is gaining more attention from the computer vision community. In this work, we aimed to build a self-supervised depth and ego-motion estimation system which can predict not only accurate depth maps and camera pose, but also camera intrinsic parameters.
  arXiv  Detail & Related papers  (2023-08-22T20:35:24Z)
• Next-generation Surgical Navigation: Marker-less Multi-view 6DoF Pose Estimation of Surgical Instruments [66.74633676595889]
  We present a multi-camera capture setup consisting of static and head-mounted cameras. Second, we publish a multi-view RGB-D video dataset of ex-vivo spine surgeries, captured in a surgical wet lab and a real operating theatre. Third, we evaluate three state-of-the-art single-view and multi-view methods for the task of 6DoF pose estimation of surgical instruments.
  arXiv  Detail & Related papers  (2023-05-05T13:42:19Z)
• Deep Selection: A Fully Supervised Camera Selection Network for Surgery Recordings [9.242157746114113]
  We use a recording system in which multiple cameras are embedded in the surgical lamp. As the embedded cameras obtain multiple video sequences, we address the task of selecting the camera with the best view of the surgery. Unlike the conventional method, which selects the camera based on the area size of the surgery field, we propose a deep neural network that predicts the camera selection probability from multiple video sequences.
  arXiv  Detail & Related papers  (2023-03-28T13:00:08Z)
• Using Hand Pose Estimation To Automate Open Surgery Training Feedback [0.0]
  This research aims to facilitate the use of state-of-the-art computer vision algorithms for the automated training of surgeons. By estimating 2D hand poses, we model the movement of the practitioner's hands, and their interaction with surgical instruments.
  arXiv  Detail & Related papers  (2022-11-13T21:47:31Z)
• RGB2Hands: Real-Time Tracking of 3D Hand Interactions from Monocular RGB Video [76.86512780916827]
  We present the first real-time method for motion capture of skeletal pose and 3D surface geometry of hands from a single RGB camera. In order to address the inherent depth ambiguities in RGB data, we propose a novel multi-task CNN. We experimentally verify the individual components of our RGB two-hand tracking and 3D reconstruction pipeline.
  arXiv  Detail & Related papers  (2021-06-22T12:53:56Z)
• Exploiting Depth Information for Wildlife Monitoring [0.0]
  We propose an automated camera trap-based approach to detect and identify animals using depth estimation. To detect and identify individual animals, we propose a novel method D-Mask R-CNN for the so-called instance segmentation. An experimental evaluation shows the benefit of the additional depth estimation in terms of improved average precision scores of the animal detection.
  arXiv  Detail & Related papers  (2021-02-10T18:10:34Z)
• Video Depth Estimation by Fusing Flow-to-Depth Proposals [65.24533384679657]
  We present an approach with a differentiable flow-to-depth layer for video depth estimation. The model consists of a flow-to-depth layer, a camera pose refinement module, and a depth fusion network. Our approach outperforms state-of-the-art depth estimation methods, and has reasonable cross dataset generalization capability.
  arXiv  Detail & Related papers  (2019-12-30T10:45:57Z)

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.