TY - GEN
T1 - Accurate and robust fully-automatic QCA
T2 - 14th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2011
AU - Hernández-Vela, Antonio
AU - Gatta, Carlo
AU - Escalera, Sergio
AU - Igual, Laura
AU - Martin-Yuste, Victoria
AU - Radeva, Petia
PY - 2011
Y1 - 2011
N2 - 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%.
AB - 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%.
KW - centerline extraction
KW - GraphCut
KW - QCA
KW - Vessel segmentation
UR - http://www.scopus.com/inward/record.url?scp=82255164525&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-23626-6_61
DO - 10.1007/978-3-642-23626-6_61
M3 - Article in proceeding
C2 - 22003736
AN - SCOPUS:82255164525
SN - 9783642236259
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 496
EP - 503
BT - Medical Image Computing and Computer-Assisted Intervention, MICCAI 2011 - 14th International Conference, Proceedings
Y2 - 18 September 2011 through 22 September 2011
ER -