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

Yong, Y.L. and Tan, L.K. and McLaughlin, R.A. and Chee, K.H. and 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), 22 (12). p. 1.

Full text not available from this repository.
Official URL: http://dx.doi.org/10.1117/1.JBO.22.12.126005

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.

Item Type: Article
Uncontrolled Keywords: Coronary lumen; Neural network; Optical coherence tomography; Optical diagnostics; Pattern recognition; Segmentation
Subjects: R Medicine
T Technology > T Technology (General)
Divisions: Faculty of Engineering
Faculty of Medicine
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 28 Jun 2018 06:46
Last Modified: 28 Jun 2018 06:46
URI: http://eprints.um.edu.my/id/eprint/18897

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