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dc.contributor.authorDevalla, Sripad Krishnaen_US
dc.contributor.authorSubramanian, Giridharen_US
dc.contributor.authorPham, Tan Hungen_US
dc.contributor.authorWang, Xiaofeien_US
dc.contributor.authorPerera, Shamiraen_US
dc.contributor.authorTun, Tin Aungen_US
dc.contributor.authorAung, Tinen_US
dc.contributor.authorSchmetterer, Leopolden_US
dc.contributor.authorThiéry, Alexandre H.en_US
dc.contributor.authorGirard, Michaël J. A.en_US
dc.identifier.citationDevalla, S. K., Subramanian, G., Pham, T. H., Wang, X., Perera, S., Tun, T. A., . . . Girard, M. J. A. (2019). A deep learning approach to Denoise optical coherence tomography images of the optic nerve head. Scientific Reports, 9(1), 14454-. doi:10.1038/s41598-019-51062-7en_US
dc.description.abstractOptical coherence tomography (OCT) has become an established clinical routine for the in vivo imaging of the optic nerve head (ONH) tissues, that is crucial in the diagnosis and management of various ocular and neuro-ocular pathologies. However, the presence of speckle noise affects the quality of OCT images and its interpretation. Although recent frame-averaging techniques have shown to enhance OCT image quality, they require longer scanning durations, resulting in patient discomfort. Using a custom deep learning network trained with 2,328 'clean B-scans' (multi-frame B-scans; signal averaged), and their corresponding 'noisy B-scans' (clean B-scans + Gaussian noise), we were able to successfully denoise 1,552 unseen single-frame (without signal averaging) B-scans. The denoised B-scans were qualitatively similar to their corresponding multi-frame B-scans, with enhanced visibility of the ONH tissues. The mean signal to noise ratio (SNR) increased from 4.02 ± 0.68 dB (single-frame) to 8.14 ± 1.03 dB (denoised). For all the ONH tissues, the mean contrast to noise ratio (CNR) increased from 3.50 ± 0.56 (single-frame) to 7.63 ± 1.81 (denoised). The mean structural similarity index (MSSIM) increased from 0.13 ± 0.02 (single frame) to 0.65 ± 0.03 (denoised) when compared with the corresponding multi-frame B-scans. Our deep learning algorithm can denoise a single-frame OCT B-scan of the ONH in under 20 ms, thus offering a framework to obtain superior quality OCT B-scans with reduced scanning times and minimal patient discomfort.en_US
dc.description.sponsorshipMOE (Min. of Education, S’pore)en_US
dc.relation.ispartofScientific Reportsen_US
dc.rights© 2019 The Author(s). This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Te images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit
dc.titleA deep learning approach to denoise optical coherence tomography images of the optic nerve headen_US
dc.typeJournal Articleen
dc.contributor.schoolLee Kong Chian School of Medicine (LKCMedicine)en_US
dc.description.versionPublished versionen_US
dc.subject.keywordsDeep Learningen_US
dc.subject.keywordsOptical Coherence Tomography (OCT)en_US
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