Self-supervised Denoising via Diffeomorphic Template Estimation: Application to Optical Coherence Tomography

by   Guillaume Gisbert, et al.

Optical Coherence Tomography (OCT) is pervasive in both the research and clinical practice of Ophthalmology. However, OCT images are strongly corrupted by noise, limiting their interpretation. Current OCT denoisers leverage assumptions on noise distributions or generate targets for training deep supervised denoisers via averaging of repeat acquisitions. However, recent self-supervised advances allow the training of deep denoising networks using only repeat acquisitions without clean targets as ground truth, reducing the burden of supervised learning. Despite the clear advantages of self-supervised methods, their use is precluded as OCT shows strong structural deformations even between sequential scans of the same subject due to involuntary eye motion. Further, direct nonlinear alignment of repeats induces correlation of the noise between images. In this paper, we propose a joint diffeomorphic template estimation and denoising framework which enables the use of self-supervised denoising for motion deformed repeat acquisitions, without empirically registering their noise realizations. Strong qualitative and quantitative improvements are achieved in denoising OCT images, with generic utility in any imaging modality amenable to multiple exposures.



There are no comments yet.


page 2

page 3

page 4

page 7


Restore from Restored: Single Image Denoising with Pseudo Clean Image

Under certain statistical assumptions of noise (e.g., zero-mean noise), ...

Self-Supervised Bulk Motion Artifact Removal in Optical Coherence Tomography Angiography

Optical coherence tomography angiography (OCTA) is an important imaging ...

Joint Optical Neuroimaging Denoising with Semantic Tasks

Optical neuroimaging is a vital tool for understanding the brain structu...

Patch2Self: Denoising Diffusion MRI with Self-Supervised Learning

Diffusion-weighted magnetic resonance imaging (DWI) is the only noninvas...

Self-supervised regression learning using domain knowledge: Applications to improving self-supervised denoising in imaging

Regression that predicts continuous quantity is a central part of applic...

Self-Supervised Deep Depth Denoising

Depth perception is considered an invaluable source of information for v...

Self-Supervised Poisson-Gaussian Denoising

We extend the blindspot model for self-supervised denoising to handle Po...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.