TY - JOUR
T1 - ARC–OCT
T2 - Automatic detection of lumen border in intravascular OCT images
AU - Cheimariotis, Grigorios Aris
AU - Chatzizisis, Yiannis S.
AU - Koutkias, Vassilis G.
AU - Toutouzas, Konstantinos
AU - Giannopoulos, Andreas
AU - Riga, Maria
AU - Chouvarda, Ioanna
AU - Antoniadis, Antonios P.
AU - Doulaverakis, Charalambos
AU - Tsamboulatidis, Ioannis
AU - Kompatsiaris, Ioannis
AU - Giannoglou, George D.
AU - Maglaveras, Nicos
PY - 2017/11
Y1 - 2017/11
N2 - Background and Objective Intravascular optical coherence tomography (OCT) is an invaluable tool for the detection of pathological features on the arterial wall and the investigation of post-stenting complications. Computational lumen border detection in OCT images is highly advantageous, since it may support rapid morphometric analysis. However, automatic detection is very challenging, since OCT images typically include various artifacts that impact image clarity, including features such as side branches and intraluminal blood presence. This paper presents ARC–OCT, a segmentation method for fully-automatic detection of lumen border in OCT images. Methods ARC–OCT relies on multiple, consecutive processing steps, accounting for image preparation, contour extraction and refinement. In particular, for contour extraction ARC–OCT employs the transformation of OCT images based on physical characteristics such as reflectivity and absorption of the tissue and, for contour refinement, local regression using weighted linear least squares and a 2nd degree polynomial model is employed to achieve artifact and small-branch correction as well as smoothness of the artery mesh. Our major focus was to achieve accurate contour delineation in the various types of OCT images, i.e., even in challenging cases with branches and artifacts. Results ARC–OCT has been assessed in a dataset of 1812 images (308 from stented and 1504 from native segments) obtained from 20 patients. ARC–OCT was compared against ground-truth manual segmentation performed by experts on the basis of various geometric features (e.g. area, perimeter, radius, diameter, centroid, etc.) and closed contour matching indicators (the Dice index, the Hausdorff distance and the undirected average distance), using standard statistical analysis methods. The proposed method was proven very efficient and close to the ground-truth, exhibiting non statistically-significant differences for most of the examined metrics. Conclusions ARC–OCT allows accurate and fully-automated lumen border detection in OCT images.
AB - Background and Objective Intravascular optical coherence tomography (OCT) is an invaluable tool for the detection of pathological features on the arterial wall and the investigation of post-stenting complications. Computational lumen border detection in OCT images is highly advantageous, since it may support rapid morphometric analysis. However, automatic detection is very challenging, since OCT images typically include various artifacts that impact image clarity, including features such as side branches and intraluminal blood presence. This paper presents ARC–OCT, a segmentation method for fully-automatic detection of lumen border in OCT images. Methods ARC–OCT relies on multiple, consecutive processing steps, accounting for image preparation, contour extraction and refinement. In particular, for contour extraction ARC–OCT employs the transformation of OCT images based on physical characteristics such as reflectivity and absorption of the tissue and, for contour refinement, local regression using weighted linear least squares and a 2nd degree polynomial model is employed to achieve artifact and small-branch correction as well as smoothness of the artery mesh. Our major focus was to achieve accurate contour delineation in the various types of OCT images, i.e., even in challenging cases with branches and artifacts. Results ARC–OCT has been assessed in a dataset of 1812 images (308 from stented and 1504 from native segments) obtained from 20 patients. ARC–OCT was compared against ground-truth manual segmentation performed by experts on the basis of various geometric features (e.g. area, perimeter, radius, diameter, centroid, etc.) and closed contour matching indicators (the Dice index, the Hausdorff distance and the undirected average distance), using standard statistical analysis methods. The proposed method was proven very efficient and close to the ground-truth, exhibiting non statistically-significant differences for most of the examined metrics. Conclusions ARC–OCT allows accurate and fully-automated lumen border detection in OCT images.
KW - Automatic segmentation
KW - Contour extraction
KW - Intravascular optical coherence tomography (OCT)
KW - Lumen–Endothelium border
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U2 - 10.1016/j.cmpb.2017.08.007
DO - 10.1016/j.cmpb.2017.08.007
M3 - Article
C2 - 28947003
AN - SCOPUS:85027682292
VL - 151
SP - 21
EP - 32
JO - Computer Methods and Programs in Biomedicine
JF - Computer Methods and Programs in Biomedicine
SN - 0169-2607
ER -