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: Contribution to conferencePaper


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
Publication statusPublished - 4 Jun 2020
EventInternational Conference on Robotics and Automation - Palais des Congrès de Paris, Paris, France
Duration: 31 May 20204 Jun 2020


ConferenceInternational Conference on Robotics and Automation
Abbreviated titleICRA 2020
Internet address


Cite this

Nguyen, A., Kundrat, D., Dagnino, G., Chi, W., Abdelaziz, M. E. M. K., Guo, Y., ... Yang, G-Z. (2020). End-to-End Real-time Catheter Segmentation with Optical Flow-Guided Warping during Endovascular Intervention. Paper presented at International Conference on Robotics and Automation, Paris, France.