TY - GEN
T1 - Endovascular Tool segmentation with multi-lateral branched network during robot-assisted catheterization
AU - Omisore, Olatunji Mumini
AU - Yi, Guanlin
AU - Zheng, Yuhong
AU - Akinyemi, Toluwanimi Oluwadara
AU - Duan, Wenke
AU - Du, Wenjing
AU - Chen, Xingyu
AU - Wang, Lei
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Robot-assisted catheterization is routinely carried out for intervention of cardiovascular diseases. Meanwhile, the success of endovascular tool navigation depends on visualization and tracking cues available in the robotic platform. Currently, real-time motion analytics are lacking, while poor illumination during fluoroscopy affects existing physics-and learning-based methods used for tool segmentation. A multi-lateral branched network (MLB-Net) is herein proposed for tool segmentation in cardiovascular angiograms. The model has an encoder with multi-lateral separable convolutions and a pyramid decoder. Model training and validation are done on 1320 angiograms obtained during robot-assisted catheterization in rabbit. Model performance, explained with F1-score of 89.01% and mean intersection-over-union of 90.05% on 330 frames, indicates the model's robustness for guidewire segmentation in angiograms. The MLB-Net offers better performance than the state-of-the-art segmentation models such as U-Net, U-Net++ and DeepLabV3. Thus, it could provide basis for endovascular tool tracking and surgical scene analytics during cardiovascular interventions.
AB - Robot-assisted catheterization is routinely carried out for intervention of cardiovascular diseases. Meanwhile, the success of endovascular tool navigation depends on visualization and tracking cues available in the robotic platform. Currently, real-time motion analytics are lacking, while poor illumination during fluoroscopy affects existing physics-and learning-based methods used for tool segmentation. A multi-lateral branched network (MLB-Net) is herein proposed for tool segmentation in cardiovascular angiograms. The model has an encoder with multi-lateral separable convolutions and a pyramid decoder. Model training and validation are done on 1320 angiograms obtained during robot-assisted catheterization in rabbit. Model performance, explained with F1-score of 89.01% and mean intersection-over-union of 90.05% on 330 frames, indicates the model's robustness for guidewire segmentation in angiograms. The MLB-Net offers better performance than the state-of-the-art segmentation models such as U-Net, U-Net++ and DeepLabV3. Thus, it could provide basis for endovascular tool tracking and surgical scene analytics during cardiovascular interventions.
KW - Deep Learning
KW - Medical Imaging
KW - Robot-assisted Catheterization
KW - Semantic Segmentation
KW - Tool Visualization
UR - http://www.scopus.com/inward/record.url?scp=85179644225&partnerID=8YFLogxK
U2 - 10.1109/EMBC40787.2023.10340692
DO - 10.1109/EMBC40787.2023.10340692
M3 - Conference contribution
C2 - 38082889
AN - SCOPUS:85179644225
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
BT - 2023 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Conference, EMBC 2023 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Conference, EMBC 2023
Y2 - 24 July 2023 through 27 July 2023
ER -
