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Guidewire Tip Tracking using U-Net with Shape and Motion Constraints

Title
Guidewire Tip Tracking using U-Net with Shape and Motion Constraints
Authors
Ullah, IhsanChikontwe, PhilipPark, Sang Hyun
DGIST Authors
Park, Sang Hyun
Issue Date
2019-02-12
Citation
1st International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2019, 215-217
Type
Conference
ISBN
9781538678220
Abstract
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.
URI
http://hdl.handle.net/20.500.11750/9812
DOI
10.1109/ICAIIC.2019.8669088
Publisher
Institute of Electrical and Electronics Engineers Inc.
Related Researcher
  • Author Park, Sang Hyun Medical Image & Signal Processing Lab
  • Research Interests 컴퓨터비전, 인공지능, 의료영상처리
Files:
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Collection:
Department of Robotics EngineeringMedical Image & Signal Processing Lab2. Conference Papers


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