Literature DB >> 27237604

Real-time localization of articulated surgical instruments in retinal microsurgery.

Nicola Rieke1, David Joseph Tan2, Chiara Amat di San Filippo3, Federico Tombari4, Mohamed Alsheakhali2, Vasileios Belagiannis5, Abouzar Eslami6, Nassir Navab2.   

Abstract

Real-time visual tracking of a surgical instrument holds great potential for improving the outcome of retinal microsurgery by enabling new possibilities for computer-aided techniques such as augmented reality and automatic assessment of instrument manipulation. Due to high magnification and illumination variations, retinal microsurgery images usually entail a high level of noise and appearance changes. As a result, real-time tracking of the surgical instrument remains challenging in in-vivo sequences. To overcome these problems, we present a method that builds on random forests and addresses the task by modelling the instrument as an articulated object. A multi-template tracker reduces the region of interest to a rectangular area around the instrument tip by relating the movement of the instrument to the induced changes on the image intensities. Within this bounding box, a gradient-based pose estimation infers the location of the instrument parts from image features. In this way, the algorithm does not only provide the location of instrument, but also the positions of the tool tips in real-time. Various experiments on a novel dataset comprising 18 in-vivo retinal microsurgery sequences demonstrate the robustness and generalizability of our method. The comparison on two publicly available datasets indicates that the algorithm can outperform current state-of-the art.
Copyright © 2016. Published by Elsevier B.V.

Keywords:  Pose estimation; Retinal microsurgery; Visual tracking

Mesh:

Year:  2016        PMID: 27237604     DOI: 10.1016/j.media.2016.05.003

Source DB:  PubMed          Journal:  Med Image Anal        ISSN: 1361-8415            Impact factor:   8.545


  4 in total

1.  CAI4CAI: The Rise of Contextual Artificial Intelligence in Computer Assisted Interventions.

Authors:  Tom Vercauteren; Mathias Unberath; Nicolas Padoy; Nassir Navab
Journal:  Proc IEEE Inst Electr Electron Eng       Date:  2019-10-23       Impact factor: 10.961

2.  Fast 5DOF needle tracking in iOCT.

Authors:  Jakob Weiss; Nicola Rieke; Mohammad Ali Nasseri; Mathias Maier; Abouzar Eslami; Nassir Navab
Journal:  Int J Comput Assist Radiol Surg       Date:  2018-03-30       Impact factor: 2.924

3.  Convolutional neural network-based surgical instrument detection.

Authors:  Tongbiao Cai; Zijian Zhao
Journal:  Technol Health Care       Date:  2020       Impact factor: 1.285

4.  An Occlusion-Aware Framework for Real-Time 3D Pose Tracking.

Authors:  Mingliang Fu; Yuquan Leng; Haitao Luo; Weijia Zhou
Journal:  Sensors (Basel)       Date:  2018-08-20       Impact factor: 3.576

  4 in total

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