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.

翻译：光学相干断层扫描（OCT）成像中固有的强散斑噪声，是准确量化分析视网膜结构的主要障碍，而视网膜结构分析对于临床诊断与疾病监测的进步至关重要。基于学习的自监督结构保持降噪方法虽展现出优于传统方法的性能，但在OCT成像中面临独特挑战。由相干A扫描光束产生的高相关性体素，因违背独立像素噪声假设，削弱了自监督学习方法的有效性。我们通过实验证明了现有模型因该独立性假设而存在的局限性。随后，我们提出了一种专门针对OCT图像去噪的新型端到端自监督学习框架，将逐切片训练模块与配准模块整合至单一网络中。针对所提方法开展了全面的消融研究。与已发表的自监督去噪模型相比，该框架展现出更优性能，有望作为预处理步骤实现更卓越的分割性能与定量分析。