title: Automatic stent detection in intravascular OCT images using bagged decision trees.
abstract: Intravascular optical coherence tomography (iOCT) is being used to assess viability of new coronary artery stent designs. We developed a highly automated method for detecting stent struts and measuring tissue coverage. We trained a bagged decision trees classifier to classify candidate struts using features extracted from the images. With 12 best features identified by forward selection, recall (precision) were 90%-94% (85%-90%). Including struts deemed insufficiently bright for manual analysis, precision improved to 94%. Strut detection statistics approached variability of manual analysis. Differences between manual and automatic area measurements were 0.12 ± 0.20 mm(2) and 0.11 ± 0.20 mm(2) for stent and tissue areas, respectively. With proposed algorithms, analyst time per stent should significantly reduce from the 6-16 hours now required.
authors.affiliation: Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, 44106,  USA.
authors.forename: Hong
authors.initials: H
authors.lastname: Lu
authors.forename: Madhusudhana
authors.initials: M
authors.lastname: Gargesha
authors.forename: Zhao
authors.initials: Z
authors.lastname: Wang
authors.forename: Daniel
authors.initials: D
authors.lastname: Chamie
authors.forename: Guilherme F
authors.initials: GF
authors.lastname: Attizzani
authors.forename: Tomoaki
authors.initials: T
authors.lastname: Kanaya
authors.forename: Soumya
authors.initials: S
authors.lastname: Ray
authors.forename: Marco A
authors.initials: MA
authors.lastname: Costa
authors.forename: Andrew M
authors.initials: AM
authors.lastname: Rollins
authors.forename: Hiram G
authors.initials: HG
authors.lastname: Bezerra
authors.forename: David L
authors.initials: DL
authors.lastname: Wilson