Linear-regression convolutional neural network for fully automated coronary lumen segmentation in intravascular optical coherence tomography
Document Type
Article
Publication Date
1-1-2017
Abstract
Intravascular optical coherence tomography (OCT) is an optical imaging modality commonly used in the assessment of coronary artery diseases during percutaneous coronary intervention. Manual segmentation to assess luminal stenosis from OCT pullback scans is challenging and time consuming. We propose a linear-regression convolutional neural network to automatically perform vessel lumen segmentation, parameterized in terms of radial distances from the catheter centroid in polar space. Benchmarked against gold-standard manual segmentation, our proposed algorithm achieves average locational accuracy of the vessel wall of 22 microns, and 0.985 and 0.970 in Dice coefficient and Jaccard similarity index, respectively. The average absolute error of luminal area estimation is 1.38%. The processing rate is 40.6 ms per image, suggesting the potential to be incorporated into a clinical workflow and to provide quantitative assessment of vessel lumen in an intraoperative time frame.
Keywords
Coronary lumen, Neural network, Optical coherence tomography, Optical diagnostics, Pattern recognition, Segmentation
Divisions
fac_eng,fac_med
Publication Title
Journal of Biomedical Optics (JBO)
Volume
22
Issue
12
Publisher
International Society for Optical Engineering (SPIE)