Linear-regression convolutional neural network for fully automated coronary lumen segmentation in intravascular optical coherence tomography

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...

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Main Authors:	Yong, Y.L., Tan, L.K., McLaughlin, R.A., Chee, K.H., Liew, Y.M.
Format:	Article
Published:	International Society for Optical Engineering (SPIE) 2017
Subjects:	R Medicine T Technology (General)
Online Access:	http://dx.doi.org/10.1117/1.JBO.22.12.126005 http://dx.doi.org/10.1117/1.JBO.22.12.126005

id	um-18897
recordtype	eprints
spelling	um-188972018-06-28T06:46:10Z Linear-regression convolutional neural network for fully automated coronary lumen segmentation in intravascular optical coherence tomography Yong, Y.L. Tan, L.K. McLaughlin, R.A. Chee, K.H. Liew, Y.M. R Medicine T Technology (General) 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. International Society for Optical Engineering (SPIE) 2017 Article PeerReviewed http://dx.doi.org/10.1117/1.JBO.22.12.126005 Yong, Y.L.; Tan, L.K.; McLaughlin, R.A.; Chee, K.H.; Liew, Y.M. (2017) Linear-regression convolutional neural network for fully automated coronary lumen segmentation in intravascular optical coherence tomography. Journal of Biomedical Optics (JBO) <http://eprints.um.edu.my/view/publication/Journal_of_Biomedical_Optics_=28JBO=29.html>, 22 (12). p. 1. http://eprints.um.edu.my/18897/
repository_type	Digital Repository
institution_category	Local University
institution	University Malaya
building	UM Research Repository
collection	Online Access
topic	R Medicine T Technology (General)
spellingShingle	R Medicine T Technology (General) Yong, Y.L. Tan, L.K. McLaughlin, R.A. Chee, K.H. Liew, Y.M. Linear-regression convolutional neural network for fully automated coronary lumen segmentation in intravascular optical coherence tomography
description	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.
format	Article
author	Yong, Y.L. Tan, L.K. McLaughlin, R.A. Chee, K.H. Liew, Y.M.
author_facet	Yong, Y.L. Tan, L.K. McLaughlin, R.A. Chee, K.H. Liew, Y.M.
author_sort	Yong, Y.L.
title	Linear-regression convolutional neural network for fully automated coronary lumen segmentation in intravascular optical coherence tomography
title_short	Linear-regression convolutional neural network for fully automated coronary lumen segmentation in intravascular optical coherence tomography
title_full	Linear-regression convolutional neural network for fully automated coronary lumen segmentation in intravascular optical coherence tomography
title_fullStr	Linear-regression convolutional neural network for fully automated coronary lumen segmentation in intravascular optical coherence tomography
title_full_unstemmed	Linear-regression convolutional neural network for fully automated coronary lumen segmentation in intravascular optical coherence tomography
title_sort	linear-regression convolutional neural network for fully automated coronary lumen segmentation in intravascular optical coherence tomography
publisher	International Society for Optical Engineering (SPIE)
publishDate	2017
url	http://dx.doi.org/10.1117/1.JBO.22.12.126005 http://dx.doi.org/10.1117/1.JBO.22.12.126005
first_indexed	2018-09-06T06:54:45Z
last_indexed	2018-09-06T06:54:45Z
_version_	1610840156633300992

Linear-regression convolutional neural network for fully automated coronary lumen segmentation in intravascular optical coherence tomography

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