Presentation + Paper
4 April 2022 Unsupervised denoising of retinal OCT with diffusion probabilistic model
Author Affiliations +
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
© (2022) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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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KEYWORDS
Denoising

Diffusion

Optical coherence tomography

Signal to noise ratio

Speckle

Image restoration

Data modeling

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