Accurate and robust fully-automatic QCA: method and numerical validation

Authors:
Antonio Hernández-Vela;Carlo Gatta;Sergio Escalera;Laura Igual;Victoria Martin-Yuste;Petia Radeva
Affiliations:
Dept. MAIA, Universitat de Barcelona, Barcelona and Centre de Visió per Computador, Bellaterra, Spain;Dept. MAIA, Universitat de Barcelona, Barcelona and Centre de Visió per Computador, Bellaterra, Spain;Dept. MAIA, Universitat de Barcelona, Barcelona and Centre de Visió per Computador, Bellaterra, Spain;Dept. MAIA, Universitat de Barcelona, Barcelona and Centre de Visió per Computador, Bellaterra, Spain;Institut Clinic del Torax, Hospital Clinic Barcelona, Barcelona, Spain;Dept. MAIA, Universitat de Barcelona, Barcelona and Centre de Visió per Computador, Bellaterra, Spain
Venue:
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part III
Year:
2011

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Abstract

The Quantitative Coronary Angiography (QCA) is a methodology used to evaluate the arterial diseases and, in particular, the degree of stenosis. In this paper we propose AQCA, a fully automatic method for vessel segmentation based on graph cut theory. Vesselness, geodesic paths and a new multi-scale edgeness map are used to compute a globally optimal artery segmentation. We evaluate the method performance in a rigorous numerical way on two datasets. The method can detect an artery with precision 92.9±5% and sensitivity 94.2±6%. The average absolute distance error between detected and ground truth centerline is 1.13 ± 0.11 pixels (about 0.27 ± 0.025mm) and the absolute relative error in the vessel caliber estimation is 2.93% with almost no bias. Moreover, the method can discriminate between arteries and catheter with an accuracy of 96.4%.