  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 small-scale
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
state-of-the-art techniques while achieving real-time performance.
