Abstract: Denoising of Home OCT Images using Noise-to-noise Trained on Artificial Eye Data

Optical coherence tomography (OCT) established as an essential part of the diagnosis, monitoring and treatment programs of patients suffering from wet age-related macular degeneration (AMD). To further improve disease progression monitoring and just-intime therapy, home OCTs such as the innovative self-examination low-cost full-field OCT (SELFF-OCT) are developed, enabling self-examination by patients due to its technical simplicity and cost efficiency, but coming at the cost of reduced image quality indicated by a low signal-to-noise ratio (SNR). Although deep learning denoising methods based on convolutional neural networks (CNN) or generative adversarial networks (GAN) achieve state-of-the-art denoising performance in improving the SNR for better image interpretability, they usually require noise-free images for training, which are not available for OCT imaging or can only be approximated by repeated scanning followed by complex and error-prone registration and multi-frame averaging processes. To circumvent this drawback, we propose a denoising approach in this work based on utilizing paired SELFF-OCT images acquired from the retina of an artificial eye to train a Noise2Noise (N2N) network by repeatedly mapping one noisy image to another noisy realization of the same image. Training of the network is performed with a small amount of data comprising only two OCT volumes. The performance of the proposed approach is evaluated by denoising unseen SELFF-OCT images from the retina of the artificial eye as well as real human eyes, utilizing standard image quality assessment (IQA) metrics like peak signal-to-noise ratio (PSNR) and structure similarity index measure (SSIM) as well as non-reference quality metrics. The qualitative and quantitative results of the evaluation verify the effectiveness of the proposed N2N approach by an improved SNR, while important structural information in the scans is preserved. Furthermore, the results reveal a superior denoising performance of the proposed approach compared to the application of conventional OCT denoising methods like block-matching and 3D filtering (BM3D) and probability-based non-local means (PNLM) [1].