Detection, grading and classification of coronary stenoses in computed tomography angiography

B. Michael Kelm, Sushil Mittal, Yefeng Zheng, Alexey Tsymbal, Dominik Bernhardt, Fernando Vega-Higuera, S. Kevin Zhou, Peter Meer, Dorin Comaniciu

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

50 Scopus citations

Abstract

Recently conducted clinical studies prove the utility of Coronary Computed Tomography Angiography (CCTA) as a viable alternative to invasive angiography for the detection of Coronary Artery Disease (CAD). This has lead to the development of several algorithms for automatic detection and grading of coronary stenoses. However, most of these methods focus on detecting calcified plaques only. A few methods that can also detect and grade non-calcified plaques require substantial user involvement. In this paper, we propose a fast and fully automatic system that is capable of detecting, grading and classifying coronary stenoses in CCTA caused by all types of plaques. We propose a four-step approach including a learning-based centerline verification step and a lumen cross-section estimation step using random regression forests. We show state-of-the-art performance of our method in experiments conducted on a set of 229 CCTA volumes. With an average processing time of 1.8 seconds per case after centerline extraction, our method is significantly faster than competing approaches.

Original languageEnglish (US)
Title of host publicationMedical Image Computing and Computer-Assisted Intervention, MICCAI 2011 - 14th International Conference, Proceedings
Pages25-32
Number of pages8
EditionPART 3
DOIs
StatePublished - 2011
Event14th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2011 - Toronto, ON, Canada
Duration: Sep 18 2011Sep 22 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 3
Volume6893 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other14th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2011
Country/TerritoryCanada
CityToronto, ON
Period9/18/119/22/11

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Fingerprint

Dive into the research topics of 'Detection, grading and classification of coronary stenoses in computed tomography angiography'. Together they form a unique fingerprint.

Cite this