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Automatic identification of coronary arteries and viewpoints in 2D x-ray angiography using deep learning (Conference Presentation)
Author(s): Tanveer F. Syeda-Mahmood; Hui Tang

Paper Abstract

The growth in volume of multi-dimensional imaging and the advent of high resolution scanners is placing a large viewing burden on clinicians. In many situations, summaries of these studies would suffice, particularly for quick viewing, easy transport and procedure planning. One easy way to organize these studies is by viewpoints depicting left and right coronary arteries. This is a difficult problem, however, requiring automated methods to (a) extract coronary arteries, (b) recognize identity of arteries as left or right coronary arteries, and recognize (c) the viewpoints from which they are taken to examine their potential pathologies. In this paper, we present a deep learning solution that addresses this problem by using a segmentation network for detection of coronary arteries and a residual deep learning network for recognizing simultaneously the viewpoint and artery identity. Results show that the deep learning method produces reliable classification for many viewpoints.

Paper Details

Date Published: 14 March 2019
Proc. SPIE 10949, Medical Imaging 2019: Image Processing, 109491A (14 March 2019); doi: 10.1117/12.2513170
Show Author Affiliations
Tanveer F. Syeda-Mahmood, IBM Research - Almaden (United States)
Hui Tang, IBM Research - Almaden (United States)

Published in SPIE Proceedings Vol. 10949:
Medical Imaging 2019: Image Processing
Elsa D. Angelini; Bennett A. Landman, Editor(s)

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