Plug-in for visualizing 3D tool tracking from videos of Minimally Invasive Surgeries

• URL: http://arxiv.org/abs/2401.09472v1
• Date: Fri, 12 Jan 2024 11:04:39 GMT
• Title: Plug-in for visualizing 3D tool tracking from videos of Minimally Invasive Surgeries
• Authors: Shubhangi Nema, Abhishek Mathur and Leena Vachhani
• Abstract summary: This paper tackles instrument tracking and 3D visualization challenges in minimally invasive surgery (MIS) Conventional and robot-assisted MIS encounter issues with limited 2D camera projections and minimal hardware integration. The proposed method involves 2D tracking based on segmentation maps, facilitating creation of labeled dataset without extensive ground-truth knowledge.
• Score: 0.6629765271909505
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: This paper tackles instrument tracking and 3D visualization challenges in minimally invasive surgery (MIS), crucial for computer-assisted interventions. Conventional and robot-assisted MIS encounter issues with limited 2D camera projections and minimal hardware integration. The objective is to track and visualize the entire surgical instrument, including shaft and metallic clasper, enabling safe navigation within the surgical environment. The proposed method involves 2D tracking based on segmentation maps, facilitating creation of labeled dataset without extensive ground-truth knowledge. Geometric changes in 2D intervals express motion, and kinematics based algorithms process results into 3D tracking information. Synthesized and experimental results in 2D and 3D motion estimates demonstrate negligible errors, validating the method for labeling and motion tracking of instruments in MIS videos. The conclusion underscores the proposed 2D segmentation technique's simplicity and computational efficiency, emphasizing its potential as direct plug-in for 3D visualization in instrument tracking and MIS practices.

Related papers

• DELTA: Dense Efficient Long-range 3D Tracking for any video [82.26753323263009]
  We introduce DELTA, a novel method that efficiently tracks every pixel in 3D space, enabling accurate motion estimation across entire videos. Our approach leverages a joint global-local attention mechanism for reduced-resolution tracking, followed by a transformer-based upsampler to achieve high-resolution predictions. Our method provides a robust solution for applications requiring fine-grained, long-term motion tracking in 3D space.
  arXiv  Detail & Related papers  (2024-10-31T17:59:01Z)
• SLAM assisted 3D tracking system for laparoscopic surgery [22.36252790404779]
  This work proposes a real-time monocular 3D tracking algorithm for post-registration tasks. Experiments from in-vivo and ex-vivo tests demonstrate that the proposed 3D tracking system provides robust 3D tracking.
  arXiv  Detail & Related papers  (2024-09-18T04:00:54Z)
• SurgTrack: CAD-Free 3D Tracking of Real-world Surgical Instruments [21.536823332387993]
  Vision-based surgical navigation has received increasing attention due to its non-invasive, cost-effective, and flexible advantages. In particular, a critical element of the vision-based navigation system is tracking surgical instruments. We propose the SurgTrack, a two-stage 3D instrument tracking method for CAD-free and robust real-world applications.
  arXiv  Detail & Related papers  (2024-09-04T10:29:59Z)
• Semi-supervised 3D Semantic Scene Completion with 2D Vision Foundation Model Guidance [11.090775523892074]
  We introduce a novel semi-supervised framework to alleviate the dependency on densely annotated data. Our approach leverages 2D foundation models to generate essential 3D scene geometric and semantic cues. Our method achieves up to 85% of the fully-supervised performance using only 10% labeled data.
  arXiv  Detail & Related papers  (2024-08-21T12:13:18Z)
• BiTrack: Bidirectional Offline 3D Multi-Object Tracking Using Camera-LiDAR Data [11.17376076195671]
  "BiTrack" is a 3D OMOT framework that includes modules of 2D-3D detection fusion, initial trajectory generation, and bidirectional trajectory re-optimization. The experiment results on the KITTI dataset demonstrate that BiTrack achieves the state-of-the-art performance for 3D OMOT tasks in terms of accuracy and efficiency.
  arXiv  Detail & Related papers  (2024-06-26T15:09:54Z)
• SpatialTracker: Tracking Any 2D Pixels in 3D Space [71.58016288648447]
  We propose to estimate point trajectories in 3D space to mitigate the issues caused by image projection. Our method, named SpatialTracker, lifts 2D pixels to 3D using monocular depth estimators. Tracking in 3D allows us to leverage as-rigid-as-possible (ARAP) constraints while simultaneously learning a rigidity embedding that clusters pixels into different rigid parts.
  arXiv  Detail & Related papers  (2024-04-05T17:59:25Z)
• MM3DGS SLAM: Multi-modal 3D Gaussian Splatting for SLAM Using Vision, Depth, and Inertial Measurements [59.70107451308687]
  We show for the first time that using 3D Gaussians for map representation with unposed camera images and inertial measurements can enable accurate SLAM. Our method, MM3DGS, addresses the limitations of prior rendering by enabling faster scale awareness, and improved trajectory tracking. We also release a multi-modal dataset, UT-MM, collected from a mobile robot equipped with a camera and an inertial measurement unit.
  arXiv  Detail & Related papers  (2024-04-01T04:57:41Z)
• HUGS: Holistic Urban 3D Scene Understanding via Gaussian Splatting [53.6394928681237]
  holistic understanding of urban scenes based on RGB images is a challenging yet important problem. Our main idea involves the joint optimization of geometry, appearance, semantics, and motion using a combination of static and dynamic 3D Gaussians. Our approach offers the ability to render new viewpoints in real-time, yielding 2D and 3D semantic information with high accuracy.
  arXiv  Detail & Related papers  (2024-03-19T13:39:05Z)
• Multi-Modal Dataset Acquisition for Photometrically Challenging Object [56.30027922063559]
  This paper addresses the limitations of current datasets for 3D vision tasks in terms of accuracy, size, realism, and suitable imaging modalities for photometrically challenging objects. We propose a novel annotation and acquisition pipeline that enhances existing 3D perception and 6D object pose datasets.
  arXiv  Detail & Related papers  (2023-08-21T10:38:32Z)
• 3D Visual Tracking Framework with Deep Learning for Asteroid Exploration [22.808962211830675]
  In this paper, we focus on the studied accurate and real-time method for 3D tracking. A new large-scale 3D asteroid tracking dataset is presented, including binocular video sequences, depth maps, and point clouds of diverse asteroids. We propose a deep-learning based 3D tracking framework, named as Track3D, which involves 2D monocular tracker and a novel light-weight amodal axis-aligned bounding-box network, A3BoxNet.
  arXiv  Detail & Related papers  (2021-11-21T04:14:45Z)
• Monocular Quasi-Dense 3D Object Tracking [99.51683944057191]
  A reliable and accurate 3D tracking framework is essential for predicting future locations of surrounding objects and planning the observer's actions in numerous applications such as autonomous driving. We propose a framework that can effectively associate moving objects over time and estimate their full 3D bounding box information from a sequence of 2D images captured on a moving platform.
  arXiv  Detail & Related papers  (2021-03-12T15:30:02Z)

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.