Unsupervised denoising of retinal OCT with diffusion probabilistic model

Dewei Hu; Yuankai K. Tao; Ipek Oguz

doi:10.1117/12.2612235

4 April 2022 Unsupervised denoising of retinal OCT with diffusion probabilistic model

Dewei Hu, Yuankai K. Tao, Ipek Oguz

Proceedings Volume 12032, Medical Imaging 2022: Image Processing; 1203206 (2022) https://doi.org/10.1117/12.2612235
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Abstract

Optical coherence tomography (OCT) is a prevalent non-invasive imaging method which provides high resolution volumetric visualization of retina. However, its inherent defect, the speckle noise, can seriously deteriorate the tissue visibility in OCT. Deep learning based approaches have been widely used for image restoration, but most of these require a noise-free reference image for supervision. In this study, we present a diffusion probabilistic model that is fully unsupervised to learn from noise instead of signal. A diffusion process is defined by adding a sequence of Gaussian noise to self-fused OCT b-scans. Then the reverse process of diffusion, modeled by a Markov chain, provides an adjustable level of denoising. Our experiment results demonstrate that our method can significantly improve the image quality with a simple working pipeline and a small amount of training data. The implementation is available at https://github.com/DeweiHu/OCT_DDPM.

Conference Presentation

Citation Download Citation

Dewei Hu, Yuankai K. Tao, and Ipek Oguz "Unsupervised denoising of retinal OCT with diffusion probabilistic model", Proc. SPIE 12032, Medical Imaging 2022: Image Processing, 1203206 (4 April 2022); https://doi.org/10.1117/12.2612235

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