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Guidewire Tip Tracking using U-Net with Shape and Motion Constraints
- Guidewire Tip Tracking using U-Net with Shape and Motion Constraints
- Ullah, Ihsan; Chikontwe, Philip; Park, Sang Hyun
- DGIST Authors
- Park, Sang Hyun
- Issue Date
- 1st International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2019, 215-217
- In recent years, research has been carried out using a micro-robot catheter instead of classic cardiac surgery performed using a catheter. To accurately control the micro-robot catheter, accurate and decisive tracking of the guidewire tip is required. In this paper, we propose a method based on the deep convolutional neural network (CNN) to track the guidewire tip. To extract a very small tip region from a large image in video sequences, we first segment small tip candidates using a segmentation CNN architecture, and then extract the best candidate using shape and motion constraints. The segmentation-based tracking strategy makes the tracking process robust and sturdy. The tracking of the guidewire tip in video sequences is performed fully-Automated in real-Time, i.e., 71 ms per image. For two-fold cross-validation, the proposed method achieves the average Dice score of 88.07% and IoU score of 85.07%. © 2019 IEEE.
- Institute of Electrical and Electronics Engineers Inc.
- Related Researcher
Park, Sang Hyun
Medical Image & Signal Processing Lab
컴퓨터비전, 인공지능, 의료영상처리
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- Department of Robotics EngineeringMedical Image & Signal Processing Lab2. Conference Papers
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