End-to-End Real-time Catheter Segmentation with Optical Flow-Guided Warping during Endovascular Intervention
This addresses the challenge of accurate catheter tracking in medical procedures, though it appears incremental by building on existing segmentation and flow methods.
The paper tackles the problem of real-time catheter segmentation for robot-assisted endovascular intervention by proposing FW-Net, which uses optical flow-guided warping to learn temporal continuity, resulting in outperforming state-of-the-art methods with real-time performance.
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.