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arXiv · 2311.15167

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

Shijie Li·Palaiologos Alexopoulos·Anse Vellappally·Ronald Zambrano·Wollstein Gadi·Guido Gerig
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Abstract

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.

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Shijie Li, Palaiologos Alexopoulos, Anse Vellappally, Ronald Zambrano, Wollstein Gadi, Guido Gerig. 2023-11-26. Self-supervised OCT Image Denoising with Slice-to-Slice Registration and Reconstruction. https://arxiv.org/abs/2311.15167

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