A Deep Learning Approach to Denoise Optical Coherence Tomography Images of the Optic Nerve Head
Sripad Krishna Devalla, Giridhar Subramanian, Tan Hung Pham, Xiaofei, Wang, Shamira Perera, Tin A. Tun, Tin Aung, Leopold Schmetterer, Alexandre H., Thiery, Michael J. A. Girard

TL;DR
This paper presents a deep learning method to effectively denoise single-frame OCT images of the optic nerve head, significantly improving image quality and potentially reducing scan times and patient discomfort.
Contribution
A novel deep learning algorithm trained to denoise OCT B-scans, enhancing image quality in under 20 milliseconds, with validated quantitative improvements over single-frame images.
Findings
Significant increase in SNR from 4.02 dB to 8.14 dB.
CNR improved from 3.50 to 7.63.
MSSIM increased from 0.13 to 0.65.
Abstract
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…
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