Open AccessFully automated side branch detection in intravascular optical coherence tomography pullback runs
Author(s) -
Ancong Wang,
Jeroen Eggermont,
Johan H. C. Reiber,
Jouke Dijkstra
Publication year - 2014
Publication title -
biomedical optics express
Language(s) - Uncategorized
Resource type - Journals
SCImago Journal Rank - 1.362
H-Index - 86
ISSN - 2156-7085
DOI - 10.1364/boe.5.003160
Subject(s) - optical coherence tomography , segmentation , computer science , intraclass correlation , artificial intelligence , lumen (anatomy) , sørensen–dice coefficient , computer vision , intravascular ultrasound , tomography , optics , image segmentation , physics , radiology , medicine , mathematics , surgery , reproducibility , statistics
Side branches in the atherosclerotic lesion region are important as they highly influence the treatment strategy selection and optimization. Moreover, they are reliable landmarks for image registration. By providing high resolution delineation of coronary morphology, intravascular optical coherence tomography (IVOCT) has been increasingly used for side branch analysis. This paper presents a fully automated method to detect side branches in IVOCT images, which relies on precise segmentation of the imaging catheter, the protective sheath, the guide wire and the lumen. 25 in-vivo data sets were used for validation. The intraclass correlation coefficient between the algorithmic results and manual delineations for the imaging catheter, the protective sheath and the lumen contour positions was 0.997, 0.949 and 0.974, respectively. All the guide wires were detected correctly and the Dice's coefficient of the shadow regions behind the guide wire was 0.97. 94.0% of 82 side branches were detected with 5.0% false positives and the Dice's coefficient of the side branch size was 0.85. In conclusion, the presented method has been demonstrated to be accurate and robust for side branch analysis.