An Improved Image Segmentation Model based on U-Net for Interventional Intravascular Robots

Yuhong Zheng, Meng Joo Er, Shiwei Shen, Wanghongbo Li, Yifa Li, Wenjing Du, Wenke Duan, Olatunji Mumini Omisore

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

3 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationProceedings - 2021 4th International Conference on Intelligent Autonomous Systems, ICoIAS 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages84-90
Number of pages7
ISBN (Electronic)9781665441957
DOIs
Publication statusPublished - May 2021
Externally publishedYes
Event4th International Conference on Intelligent Autonomous Systems, ICoIAS 2021 - Wuhan, China
Duration: 14 May 202116 May 2021

Publication series

NameProceedings - 2021 4th International Conference on Intelligent Autonomous Systems, ICoIAS 2021

Conference

Conference4th International Conference on Intelligent Autonomous Systems, ICoIAS 2021
Country/TerritoryChina
CityWuhan
Period14/05/2116/05/21

Keywords

  • angiogram dataset
  • guidewire
  • robot catheter systems
  • semantic segmentation
  • vascular intervention

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Control and Optimization
  • Instrumentation

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