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

Authors:
B. Michael Kelm;Sushil Mittal;Yefeng Zheng;Alexey Tsymbal;Dominik Bernhardt;Fernando Vega-Higuera;S. Kevin Zhou;Peter Meer;Dorin Comaniciu
Affiliations:
Image Analytics and Informatics, Corporate Technology, Siemens AG, Erlangen, Germany;Image Analytics and Informatics, Siemens Corporate Research, Princeton and Electrical and Computer Engineering, Rutgers University, NJ;Image Analytics and Informatics, Siemens Corporate Research, Princeton, NJ;Image Analytics and Informatics, Corporate Technology, Siemens AG, Erlangen, Germany;Computed Tomography, Healthcare Sector, Siemens AG, Forchheim, Germany;Computed Tomography, Healthcare Sector, Siemens AG, Forchheim, Germany;Image Analytics and Informatics, Siemens Corporate Research, Princeton, NJ;Electrical and Computer Engineering, Rutgers University, NJ;Image Analytics and Informatics, Siemens Corporate Research, Princeton, NJ
Venue:
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part III
Year:
2011

Citing 4
Cited 2

Random Forests

Machine Learning
Optimal Surface Segmentation in Volumetric Images-A Graph-Theoretic Approach

IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Vessel Tree Modeling

MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
Fast automatic detection of calcified coronary lesions in 3d cardiac CT images

MLMI'10 Proceedings of the First international conference on Machine learning in medical imaging

Decision Forests: A Unified Framework for Classification, Regression, Density Estimation, Manifold Learning and Semi-Supervised Learning

Foundations and Trends® in Computer Graphics and Vision
A supervised learning based approach to detect crohn's disease in abdominal MR volumes

MICCAI'12 Proceedings of the 4th international conference on Abdominal Imaging: computational and clinical applications

Quantified Score

Hi-index	0.00

Visualization

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 crosssection 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.