Methodology for fully automated segmentation and plaque characterization in intracoronary optical coherence tomography images

Optical coherence tomography (OCT) is a light-based intracoronary imaging modality that provides high-resolution cross-sectional images of the luminal and plaque morphology. Currently, the segmentation of OCT images and identification of the composition of plaque are mainly performed manually by exp...

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Bibliographic Details
Published in:Journal of biomedical optics 2014-02, Vol.19 (2), p.026009-026009
Main Authors: Athanasiou, Lambros S, Bourantas, Christos V, Rigas, George, Sakellarios, Antonis I, Exarchos, Themis P, Siogkas, Panagiotis K, Ricciardi, Andrea, Naka, Katerina K, Papafaklis, Michail I, Michalis, Lampros K, Prati, Francesco, Fotiadis, Dimitrios I
Format: Article
Language:English
Subjects:
Algorithms
Annotations
Automated
Calcium
Calcium - chemistry
Humans
Image Processing, Computer-Assisted - methods
Methodology
Observers
Optical Coherence Tomography
Plaque, Atherosclerotic - chemistry
Plaque, Atherosclerotic - pathology
Reproducibility of Results
Segmentation
Tomography, Optical Coherence - methods
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Summary:Optical coherence tomography (OCT) is a light-based intracoronary imaging modality that provides high-resolution cross-sectional images of the luminal and plaque morphology. Currently, the segmentation of OCT images and identification of the composition of plaque are mainly performed manually by expert observers. However, this process is laborious and time consuming and its accuracy relies on the expertise of the observer. To address these limitations, we present a methodology that is able to process the OCT data in a fully automated fashion. The proposed methodology is able to detect the lumen borders in the OCT frames, identify the plaque region, and detect four tissue types: calcium (CA), lipid tissue (LT), fibrous tissue (FT), and mixed tissue (MT). The efficiency of the developed methodology was evaluated using annotations from 27 OCT pullbacks acquired from 22 patients. High Pearson's correlation coefficients were obtained between the output of the developed methodology and the manual annotations (from 0.96 to 0.99), while no significant bias with good limits of agreement was shown in the Bland-Altman analysis. The overlapping areas ratio between experts' annotations and methodology in detecting CA, LT, FT, and MT was 0.81, 0.71, 0.87, and 0.81, respectively.
ISSN:1083-3668
1560-2281
DOI:10.1117/1.JBO.19.2.026009