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

Yan Ling Yong; Li Kuo Tan; Robert A. McLaughlin; Kok Han Chee; Yih Miin Liew

doi:10.1117/1.JBO.22.12.126005

23 December 2017 Linear-regression convolutional neural network for fully automated coronary lumen segmentation in intravascular optical coherence tomography

Yan Ling Yong, Li Kuo Tan, Robert A. McLaughlin, Kok Han Chee, Yih Miin Liew

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Journal of Biomedical Optics, Vol. 22, Issue 12, 126005 (December 2017). https://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.

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Yan Ling Yong, Li Kuo Tan, Robert A. McLaughlin, Kok Han Chee, and Yih Miin Liew "Linear-regression convolutional neural network for fully automated coronary lumen segmentation in intravascular optical coherence tomography," Journal of Biomedical Optics 22(12), 126005 (23 December 2017). https://doi.org/10.1117/1.JBO.22.12.126005

Received: 5 October 2017; Accepted: 1 December 2017; Published: 23 December 2017

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