title: A bifurcation identifier for IV-OCT using orthogonal least squares and supervised machine learning.
abstract: Intravascular optical coherence tomography (IV-OCT) is an in-vivo imaging modality based on the intravascular introduction of a catheter which provides a view of the inner wall of blood vessels with a spatial resolution of 10-20 μm. Recent studies in IV-OCT have demonstrated the importance of the bifurcation regions. Therefore, the development of an automated tool to classify hundreds of coronary OCT frames as bifurcation or nonbifurcation can be an important step to improve automated methods for atherosclerotic plaques quantification, stent analysis and co-registration between different modalities. This paper describes a fully automated method to identify IV-OCT frames in bifurcation regions. The method is divided into lumen detection; feature extraction; and classification, providing a lumen area quantification, geometrical features of the cross-sectional lumen and labeled slices. This classification method is a combination of supervised machine learning algorithms and feature selection using orthogonal least squares methods. Training and tests were performed in sets with a maximum of 1460 human coronary OCT frames. The lumen segmentation achieved a mean difference of lumen area of 0.11 mm(2) compared with manual segmentation, and the AdaBoost classifier presented the best result reaching a F-measure score of 97.5% using 104 features.
authors.affiliation: Division of Informatics, Heart Institute (InCor), University of São Paulo Medical School, Av. Dr. Eneas de Carvalho, 44, cep:05403-900 São Paulo, Brazil. Electronic address: maysa@ime.usp.br.
authors.forename: Maysa M G
authors.initials: MM
authors.lastname: Macedo
authors.affiliation: Hemodynamics, Heart Institute (InCor), University of São Paulo Medical School, Av. Dr. Eneas de Carvalho, 44, cep:05403-900 São Paulo, Brazil.
authors.forename: Welingson V N
authors.initials: WV
authors.lastname: Guimarães
authors.affiliation: Hemodynamics, Heart Institute (InCor), University of São Paulo Medical School, Av. Dr. Eneas de Carvalho, 44, cep:05403-900 São Paulo, Brazil.
authors.forename: Micheli Z
authors.initials: MZ
authors.lastname: Galon
authors.affiliation: Hemodynamics, Heart Institute (InCor), University of São Paulo Medical School, Av. Dr. Eneas de Carvalho, 44, cep:05403-900 São Paulo, Brazil.
authors.forename: Celso K
authors.initials: CK
authors.lastname: Takimura
authors.affiliation: Hemodynamics, Heart Institute (InCor), University of São Paulo Medical School, Av. Dr. Eneas de Carvalho, 44, cep:05403-900 São Paulo, Brazil.
authors.forename: Pedro A
authors.initials: PA
authors.lastname: Lemos
authors.affiliation: Division of Informatics, Heart Institute (InCor), University of São Paulo Medical School, Av. Dr. Eneas de Carvalho, 44, cep:05403-900 São Paulo, Brazil.
authors.forename: Marco Antonio
authors.initials: MA
authors.lastname: Gutierrez
mesh_terms: D003324:Coronary Artery Disease / Q000000981:diagnostic imaging*; D003627:Data Interpretation, Statistical; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016018:Least-Squares Analysis; D010363:Pattern Recognition, Automated / Q000379:methods*; D011859:Radiography; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D000069553:Supervised Machine Learning; D041623:Tomography, Optical Coherence / Q000379:methods*
keywords: Bifurcation; Classification; Intravascular; Machine learning; Optical coherence tomography; Orthogonal least squares; Segmentation