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

arXiv: https://arxiv.org/abs/1809.10589 · HTML: https://mlsanity.com/paper/1809.10589/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Sep 2018.

## Built on by (most cited first, 11 of 11)

- [SDnDTI: Self-supervised deep learning-based denoising for diffusion tensor MRI](https://mlsanity.com/paper/2111.07220/) (2021)
- [Unsupervised Denoising of Retinal OCT with Diffusion Probabilistic Model](https://mlsanity.com/paper/2201.11760/) (2022)
- [LIFE: A Generalizable Autodidactic Pipeline for 3D OCT-A Vessel Segmentation](https://mlsanity.com/paper/2107.04282/) (2021)
- [Retinal OCT Denoising with Pseudo-Multimodal Fusion Network](https://mlsanity.com/paper/2107.04288/) (2021)
- [Domain-Aware Few-Shot Learning for Optical Coherence Tomography Noise Reduction](https://mlsanity.com/paper/2306.08102/) (2023)
- [Automatic Segmentation of the Optic Nerve Head Region in Optical Coherence Tomography: A Methodological Review](https://mlsanity.com/paper/2109.02322/) (2021)
- [Self-Supervised Bulk Motion Artifact Removal in Optical Coherence Tomography Angiography](https://mlsanity.com/paper/2202.10360/) (2022)
- [Advances in Electron Microscopy with Deep Learning](https://mlsanity.com/paper/2101.01178/) (2021)
- [ADC-Net: An Open-Source Deep Learning Network for Automated Dispersion Compensation in Optical Coherence Tomography](https://mlsanity.com/paper/2201.12625/) (2022)
- [Enhanced Denoising of Optical Coherence Tomography Images Using Residual U-Net](https://mlsanity.com/paper/2407.13090/) (2024)
- [Pre-training Enables Extraordinary All-optical Image Denoising](https://mlsanity.com/paper/2605.07810/) (2026)

## Abstract (arXiv)

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 ± 0.68 dB (single-frame) to 8.14 ± 1.03 dB (denoised). For all the ONH tissues, the mean CNR increased from 3.50 ± 0.56 (single-frame) to 7.63 ± 1.81 (denoised). The MSSIM increased from 0.13 ± 0.02 (single frame) to 0.65 ± 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.