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

• URL: http://arxiv.org/abs/2006.09117v1
• Date: Tue, 16 Jun 2020 12:53:27 GMT
• Title: End-to-End Real-time Catheter Segmentation with Optical Flow-Guided Warping during Endovascular Intervention
• Authors: Anh Nguyen, Dennis Kundrat, Giulio Dagnino, Wenqiang Chi, Mohamed E. M. K. Abdelaziz, Yao Guo, YingLiang Ma, Trevor M. Y. Kwok, Celia Riga, and Guang-Zhong Yang
• Abstract summary: We present FW-Net, an end-to-end and real-time deep learning framework for endovascular intervention. 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.
• Score: 26.467626509096043
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 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.

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