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Articulated Multi-Instrument 2D Pose Estimation Using Fully Convolutional Networks

Du, X; Kurmann, T; Chang, PL; Allan, M; Ourselin, S; Sznitman, R; Kelly, J; (2018) Articulated Multi-Instrument 2D Pose Estimation Using Fully Convolutional Networks. IEEE Transactions on Medical Imaging , 37 (5) pp. 1276-1287. 10.1109/TMI.2017.2787672. Green open access

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Abstract

Instrument detection, pose estimation and tracking in surgical videos is an important vision component for computer assisted interventions. While significant advances have been made in recent years, articulation detection is still a major challenge. In this paper, we propose a deep neural network for articulated multi-instrument 2D pose estimation, which is trained on a detailed annotations of endoscopic and microscopic datasets. Our model is formed by a fully convolutional detection-regression network. Joints and associations between joint pairs in our instrument model are located by the detection subnetwork and are subsequently refined through a regression subnetwork. Based on the output from the model, the poses of the instruments are inferred using maximum bipartite graph matching. Our estimation framework is powered by deep learning techniques without any direct kinematic information from a robot. Our framework is tested on single-instrument RMIT data, and also on multi-instrument EndoVis and in vivo data with promising results. In addition, the dataset annotations are publicly released along with our code and model.

Type: Article
Title: Articulated Multi-Instrument 2D Pose Estimation Using Fully Convolutional Networks
Location: UK
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/TMI.2017.2787672
Publisher version: https://doi.org/10.1109/TMI.2017.2787672
Language: English
Additional information: This work is licensed under a Creative Commons Attribution 3.0 License. For more information, see http://creativecommons.org/licenses/by/3.0/.
Keywords: Surgical instrument detection, articulated pose estimation, fully convolutional networks, surgical vision
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Surgery and Interventional Sci
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Surgery and Interventional Sci > Department of Targeted Intervention
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10041612
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