Abstract

Optical 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.

Details

Title
A Deep Learning Approach to Denoise Optical Coherence Tomography Images of the Optic Nerve Head
Author
Devalla, Sripad Krishna 1 ; Subramanian, Giridhar 1 ; Pham, Tan Hung 2 ; Wang, Xiaofei 3 ; Perera, Shamira 4 ; Tun, Tin A 2 ; Aung, Tin 4 ; Schmetterer, Leopold 5 ; Thiéry, Alexandre H 6 ; Girard, Michaël J A 2 

 Ophthalmic Engineering & Innovation Laboratory, Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, Singapore, Singapore 
 Ophthalmic Engineering & Innovation Laboratory, Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, Singapore, Singapore; Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore 
 Ophthalmic Engineering & Innovation Laboratory, Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, Singapore, Singapore; Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China 
 Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore; Duke-NUS Graduate Medical School, Singapore, Singapore 
 Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore; Nanyang Technological University, Jurong West, Singapore; Department of Clinical Pharmacology, Medical University of Vienna, Vienna, Austria; Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria 
 Department of Statistics and Applied Probability, National University of Singapore, Singapore, Singapore 
Pages
1-13
Publication year
2019
Publication date
Oct 2019
Publisher
Nature Publishing Group
e-ISSN
20452322
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2302414756
Copyright
© 2019. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.