TY - GEN
T1 - An Improved Image Segmentation Model based on U-Net for Interventional Intravascular Robots
AU - Zheng, Yuhong
AU - Er, Meng Joo
AU - Shen, Shiwei
AU - Li, Wanghongbo
AU - Li, Yifa
AU - Du, Wenjing
AU - Duan, Wenke
AU - Omisore, Olatunji Mumini
N1 - Funding Information:
ACKNOWLEDGMENT This work was supported by the National Outstanding Youth Science Fund Project of National Natural Science Foundation of China (#61950410618) and Shenzhen Natural Science Foundation (#JCYJ20190812173205538).
Publisher Copyright:
© 2021 IEEE.
PY - 2021/5
Y1 - 2021/5
N2 - Robot-assisted intervention vascular procedures have become an important research focus in recent years. The robotic systems involve separate devices designed for master-slave operation, and this greatly reduces exposure of interventionists to radiation. However, image-based visualization and tracking of endovascular tools have received minimal efforts. In this study, an end-end deep learning model is developed for segmentation of guidewire in X-ray angiograms acquired during robot-assisted intravascular catheterization. The encoding module in original U-net was adopted while a simplified decoding network module was employed for binary pixel classification. For this purpose, custom isomorphic master-slave robotic system and C-arm cone beam computed tomography (CBCT) imaging system were employed for auricle-to-femoral cardiac catheterizations done in rabbits, while a binary dataset including X-ray angiograms was recroded and used for training the improved U-Net model. Validation of the modified U-Net model shows it performs better than other semantic segmentation networks. These include MIoU and feedforword speed (Ffs) of 2.23% and 113.5%, respectively; these are higher than those achieved by the original U-Net. This study provides a new binary dataset for guidewire segmentation.
AB - Robot-assisted intervention vascular procedures have become an important research focus in recent years. The robotic systems involve separate devices designed for master-slave operation, and this greatly reduces exposure of interventionists to radiation. However, image-based visualization and tracking of endovascular tools have received minimal efforts. In this study, an end-end deep learning model is developed for segmentation of guidewire in X-ray angiograms acquired during robot-assisted intravascular catheterization. The encoding module in original U-net was adopted while a simplified decoding network module was employed for binary pixel classification. For this purpose, custom isomorphic master-slave robotic system and C-arm cone beam computed tomography (CBCT) imaging system were employed for auricle-to-femoral cardiac catheterizations done in rabbits, while a binary dataset including X-ray angiograms was recroded and used for training the improved U-Net model. Validation of the modified U-Net model shows it performs better than other semantic segmentation networks. These include MIoU and feedforword speed (Ffs) of 2.23% and 113.5%, respectively; these are higher than those achieved by the original U-Net. This study provides a new binary dataset for guidewire segmentation.
KW - angiogram dataset
KW - guidewire
KW - robot catheter systems
KW - semantic segmentation
KW - vascular intervention
UR - http://www.scopus.com/inward/record.url?scp=85115836718&partnerID=8YFLogxK
U2 - 10.1109/ICoIAS53694.2021.00023
DO - 10.1109/ICoIAS53694.2021.00023
M3 - Conference contribution
AN - SCOPUS:85115836718
T3 - Proceedings - 2021 4th International Conference on Intelligent Autonomous Systems, ICoIAS 2021
SP - 84
EP - 90
BT - Proceedings - 2021 4th International Conference on Intelligent Autonomous Systems, ICoIAS 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 4th International Conference on Intelligent Autonomous Systems, ICoIAS 2021
Y2 - 14 May 2021 through 16 May 2021
ER -