title: Segmentation method of intravascular ultrasound images of human coronary arteries.
abstract: The goal of this study was to show the feasibility of a 2D segmentation fast-marching method (FMM) in the context of intravascular ultrasound (IVUS) imaging of coronary arteries. The original FMM speed function combines gradient-based contour information and region information, that is the gray level probability density functions of the vessel structures, that takes into account the variability in appearance of the tissues and the lumen in IVUS images acquired at 40 MHz. Experimental results on 38 in vivo IVUS sequences yielded mean point-to-point distances between detected vessel wall boundaries and manual validation contours below 0.11 mm, and Hausdorff distances below 0.33 mm, as evaluated on 3207 images. The proposed method proved to be robust in taking into account various artifacts in ultrasound images: partial shadowing due to calcium inclusions within the plaque, side branches adjacent to the main artery to segment, the presence of a stent, injection of contrast agent or dissection, as tested on 209 images presenting such artifacts.
authors.affiliation: Laboratory of Biorheology and Medical Ultrasonics, University of Montreal Hospital Research Center (CRCHUM), Montreal, Canada.
authors.forename: François
authors.initials: F
authors.lastname: Destrempes
authors.affiliation: Laboratory of Biorheology and Medical Ultrasonics, University of Montreal Hospital Research Center (CRCHUM), Montreal, Canada.
authors.forename: Marie-Hélène
authors.initials: MH
authors.lastname: Roy Cardinal
authors.affiliation: Laboratory of Biorheology and Medical Ultrasonics, University of Montreal Hospital Research Center (CRCHUM), Montreal, Canada.
authors.forename: Louise
authors.initials: L
authors.lastname: Allard
authors.affiliation: Montreal Heart Institute, Montreal, Canada.
authors.forename: Jean-Claude
authors.initials: JC
authors.lastname: Tardif
authors.affiliation: Laboratory of Biorheology and Medical Ultrasonics, University of Montreal Hospital Research Center (CRCHUM), Montreal, Canada; Department of Radiology, Radio-Oncology and Nuclear Medicine, University of Montreal, Montreal, Canada; Institute of Biomedical Engineering, University of Montreal, Montreal, Canada. Electronic address: guy.cloutier@umontreal.ca.
authors.forename: Guy
authors.initials: G
authors.lastname: Cloutier
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D023921:Coronary Stenosis / Q000000981:diagnostic imaging*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D018084:Ultrasonography, Interventional / Q000379:methods*
keywords: Cardiovascular imaging; Segmentation; Statistical analysis; Ultrasound; Validation