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

• URL: http://arxiv.org/abs/2311.15167v2
• Date: Fri, 8 Dec 2023 16:40:26 GMT
• Title: Self-supervised OCT Image Denoising with Slice-to-Slice Registration and Reconstruction
• Authors: Shijie Li, Palaiologos Alexopoulos, Anse Vellappally, Ronald Zambrano, Wollstein Gadi, Guido Gerig
• Abstract summary: Learning-based self-supervised methods for structure-preserving noise reduction have demonstrated superior performance over traditional methods. We introduce a new end-to-end self-supervised learning framework specifically tailored for OCT image denoising.
• Score: 5.972377737617966
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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