Self-supervised OCT Image Denoising with Slice-to-Slice Registration and Reconstruction

Strong speckle noise is inherent to optical coherence tomography (OCT) imaging and represents a significant obstacle for accurate quantitative analysis of retinal structures which is key for advances in clinical diagnosis and monitoring of disease. Learning-based self-supervised methods for structur...

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Bibliographic Details
Published in:arXiv.org 2023-12
Main Authors: Li, Shijie, Alexopoulos, Palaiologos, Vellappally, Anse, Zambrano, Ronald, Wollstein Gadi, Gerig, Guido
Format: Article
Language:English
Subjects:
Ablation
Image reconstruction
Medical imaging
Noise reduction
Optical Coherence Tomography
Quantitative analysis
Self-supervised learning
Online Access:Get full text
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Summary:Strong speckle noise is inherent to optical coherence tomography (OCT) imaging and represents a significant obstacle for accurate quantitative analysis of retinal structures which is key for advances in clinical diagnosis and monitoring of disease. Learning-based self-supervised methods for structure-preserving noise reduction have demonstrated superior performance over traditional methods but face unique challenges in OCT imaging. The high correlation of voxels generated by coherent A-scan beams undermines the efficacy of self-supervised learning methods as it violates the assumption of independent pixel noise. We conduct experiments demonstrating limitations of existing models due to this independence assumption. We then introduce a new end-to-end self-supervised learning framework specifically tailored for OCT image denoising, integrating slice-by-slice training and registration modules into one network. An extensive ablation study is conducted for the proposed approach. Comparison to previously published self-supervised denoising models demonstrates improved performance of the proposed framework, potentially serving as a preprocessing step towards superior segmentation performance and quantitative analysis.
ISSN:2331-8422