Automated Stenosis Detection and Classification in X-ray Angiography Using Deep Neural Network

Chao Cong, Yoko Kato, Henrique Doria Vasconcellos, Joao Lima, Bharath Venkatesh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper proposes a deep-learning based workflow for stenosis classification and localization on coronary angiography images of 194 patients from a multi-center study. Coronary stenosis severities were categorized into three groups of <25% stenosis, 25 to 99% stenosis, total occlusion as 3-CAT and two groups of <25% stenosis, >25% stenosis as 2-CAT for classification training labels; and stenosis bounding boxes were annotated in images as stenosis localization labels, based on expert physician's visual reading on right coronary artery (RCA) and left coronary artery (LCA) images with full contrast filling of the coronary artery. CNN and recurrent neural network models were employed for coronary artery view classification, candidate frame selection and stenosis classification. Furthermore, stenosis activation maps were implemented for weakly-supervised stenoses positioning. In experiments, our method achieved 0.91/0.85 AUC values for 3-CAT stenosis classification in RCA and LCA respectively; and 0.91/0.87 AUC values for 2-CAT classification in RCA and LCA respectively. For stenosis detection on most significant regions, sensitivity for RCA/LCA were 0.72/0.60 respectively; and mean square error between detection and ground-truth center points were 69.6/79.5 pixels for RCA/LCA in image with size of 512. The results show our method achieves high performance in stenosis severity classification and performs reasonably well for stenosis positioning.

Original languageEnglish (US)
Title of host publicationProceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019
EditorsIllhoi Yoo, Jinbo Bi, Xiaohua Tony Hu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1301-1308
Number of pages8
ISBN (Electronic)9781728118673
DOIs
StatePublished - Nov 2019
Event2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019 - San Diego, United States
Duration: Nov 18 2019Nov 21 2019

Publication series

NameProceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019

Conference

Conference2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019
CountryUnited States
CitySan Diego
Period11/18/1911/21/19

Keywords

  • Convolutional Neural Network
  • Coronary Artery Disease
  • Image Classification
  • Stenosis Positioning
  • X-ray Angiography

ASJC Scopus subject areas

  • Biochemistry
  • Biotechnology
  • Molecular Medicine
  • Modeling and Simulation
  • Health Informatics
  • Pharmacology (medical)
  • Public Health, Environmental and Occupational Health

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  • Cite this

    Cong, C., Kato, Y., Vasconcellos, H. D., Lima, J., & Venkatesh, B. (2019). Automated Stenosis Detection and Classification in X-ray Angiography Using Deep Neural Network. In I. Yoo, J. Bi, & X. T. Hu (Eds.), Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019 (pp. 1301-1308). [8983033] (Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM47256.2019.8983033