A Deep Learning Approach to Denoise Optical Coherence Tomography Images of the Optic Nerve Head

• Published in: arXiv.org 2018-09
• Main Authors: Devalla, Sripad Krishna, Subramanian, Giridhar, Pham, Tan Hung, Wang, Xiaofei, Perera, Shamira, Tun, Tin A, Aung, Tin, Schmetterer, Leopold, Thiery, Alexandre H, Girard, Michael J A
• Format: Article
• Language: English
• Subjects: Algorithms; Deep learning; Noise; Noise reduction; Optical Coherence Tomography; Optics; Random noise; Signal averaging; Signal to noise ratio; Tomography; Visibility
• ISSN: 2331-8422

Purpose: To develop a deep learning approach to de-noise optical coherence tomography (OCT) B-scans of the optic nerve head (ONH). Methods: Volume scans consisting of 97 horizontal B-scans were acquired through the center of the ONH using a commercial OCT device (Spectralis) for both eyes of 20 subjects. For each eye, single-frame (without signal averaging), and multi-frame (75x signal averaging) volume scans were obtained. A custom deep learning network was then designed and trained with 2,328 "clean B-scans" (multi-frame B-scans), and their corresponding "noisy B-scans" (clean B-scans + gaussian noise) to de-noise the single-frame B-scans. The performance of the de-noising algorithm was assessed qualitatively, and quantitatively on 1,552 B-scans using the signal to noise ratio (SNR), contrast to noise ratio (CNR), and mean structural similarity index metrics (MSSIM). Results: The proposed algorithm successfully denoised unseen single-frame OCT 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 SNR increased from \(4.02 \pm 0.68\) dB (single-frame) to \(8.14 \pm 1.03\) dB (denoised). For all the ONH tissues, the mean CNR increased from \(3.50 \pm 0.56\) (single-frame) to \(7.63 \pm 1.81\) (denoised). The MSSIM increased from \(0.13 \pm 0.02\) (single frame) to \(0.65 \pm 0.03\) (denoised) when compared with the corresponding multi-frame B-scans. Conclusions: 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.