title: Accurate and robust fully-automatic QCA: method and numerical validation.
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 +/- 0.025 mm) 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%.
authors: Dept. MAIA, Universitat de Barcelona, Gran Via 585, 08007 Barcelona, Spain. ahernandez@cvc.uab.cat
authors: Antonio
authors: A
authors: Hernández-Vela
authors: Carlo
authors: C
authors: Gatta
authors: Sergio
authors: S
authors: Escalera
authors: Laura
authors: L
authors: Igual
authors: Victoria
authors: V
authors: Martin-Yuste
authors: Petia
authors: P
authors: Radeva
mesh_terms: D000465:Algorithms; D001331:Automation; D017023:Coronary Angiography / Q000379:methods*; D003952:Diagnostic Imaging / Q000379:methods*; D006321:Heart / Q000502:physiology; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D015233:Models, Statistical; D008962:Models, Theoretical; D019047:Phantoms, Imaging; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity