End-to-End Real-time Catheter Segmentation with Optical Flow-Guided Warping during Endovascular Intervention

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Nguyen, Anh, Kundrat, Dennis, Dagnino, Giulio, Chi, Wenqiang, Abdelaziz, Mohamed E. M. K., Guo, Yao, Ma, YingLiang ORCID: https://orcid.org/0000-0001-5770-5843, Kwok, Trevor M. Y., Riga, Celia and Yang, Guang-Zhong (2020) End-to-End Real-time Catheter Segmentation with Optical Flow-Guided Warping during Endovascular Intervention. In: 2020 IEEE International Conference on Robotics and Automation. The Institute of Electrical and Electronics Engineers (IEEE), pp. 9967-9973. ISBN 978-1-7281-7395-5

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Abstract

Accurate real-time catheter segmentation is an important pre-requisite for robot-assisted endovascular intervention. Most of the existing learning-based methods for catheter segmentation and tracking are only trained on smallscale datasets or synthetic data due to the difficulties of ground-truth annotation. Furthermore, the temporal continuity in intraoperative imaging sequences is not fully utilised. In this paper, we present FW-Net, an end-to-end and real-time deep learning framework for endovascular intervention. The proposed FW-Net has three modules: a segmentation network with encoder-decoder architecture, a flow network to extract optical flow information, and a novel flow-guided warping function to learn the frame-to-frame temporal continuity. We show that by effectively learning temporal continuity, the network can successfully segment and track the catheters in real-time sequences using only raw ground-truth for training. Detailed validation results confirm that our FW-Net outperforms stateof- the-art techniques while achieving real-time performance.

Item Type: Book Section
Additional Information: Funding Information: This research is supported by the UK Engineering and Physical Science Research Council (EP/N024877/1) and the Wellcome Trust.
Uncontrolled Keywords: software,control and systems engineering,artificial intelligence,electrical and electronic engineering ,/dk/atira/pure/subjectarea/asjc/1700/1712
Faculty \ School: Faculty of Science > School of Computing Sciences
UEA Research Groups: Faculty of Science > Research Groups > Norwich Epidemiology Centre
Faculty of Medicine and Health Sciences > Research Groups > Norwich Epidemiology Centre
Faculty of Science > Research Groups > Data Science and AI
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Depositing User: LivePure Connector
Date Deposited: 06 Jan 2023 11:31
Last Modified: 10 Dec 2024 01:12
URI: https://ueaeprints.uea.ac.uk/id/eprint/90440
DOI: 10.1109/ICRA40945.2020.9197307

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