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

Anh Nguyen, Dennis Kundrat, Giulio Dagnino, Wenqiang Chi, Mohamed E. M. K. Abdelaziz, Yao Guo, YingLiang Ma, Trevor M. Y. Kwok, Celia Riga, Guang-Zhong Yang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Citations (Scopus)

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.
Original languageEnglish
Title of host publication2020 IEEE International Conference on Robotics and Automation
Subtitle of host publicationICRA 2020
PublisherThe Institute of Electrical and Electronics Engineers (IEEE)
Pages9967-9973
Number of pages7
ISBN (Electronic)9781728173955
ISBN (Print)978-1-7281-7395-5, 978-1-7281-7395-5
DOIs
Publication statusPublished - 15 Sep 2020

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