Spectral-domain optical coherence tomography (SD-OCT) images inevitably suffer from multiplicative speckle noise caused by random interference. This study proposes an unsupervised domain adaptation approach for noise reduction by translating the SD-OCT to the corresponding high-quality enhanced depth imaging (EDI)-OCT. We propose a structure-persevered cycle-consistent generative adversarial network for unpaired image-to-image translation, which can be applied to imbalanced unpaired data, and can effectively preserve retinal details based on a structure-specific cross-domain description. It also imposes smoothness by penalizing the intensity variation of the low reflective region between consecutive slices. Our approach was tested on a local data set that consisted of 268 SD-OCT volumes and two public independent validation datasets including 20 SD-OCT volumes and 17 B-scans, respectively. Experimental results show that our method can effectively suppress noise and maintain the retinal structure, compared with other traditional approaches and deep learning methods in terms of qualitative and quantitative assessments. Our proposed method shows good performance for speckle noise reduction and can assist downstream tasks of OCT analysis.

中文翻译：

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使用保留结构的域转移方法降低SD-OCT的噪声。

光谱域光学相干断层扫描（SD-OCT）图像不可避免地遭受由随机干扰引起的成倍斑点噪声。这项研究提出了一种无监督的域自适应方法，通过将SD-OCT转换为相应的高质量增强深度成像（EDI）-OCT来降低噪声。我们提出了一种结构持久的周期一致的生成对抗性网络，用于不成对的图像到图像的转换，该网络可以应用于不成对的不成对数据，并且可以基于特定于结构的跨域描述有效地保留视网膜细节。它还通过惩罚连续切片之间的低反射区域的强度变化来增强平滑度。我们的方法在包含268个SD-OCT卷和两个公共独立验证数据集的本地数据集上进行了测试，分别包括20个SD-OCT卷和17个B扫描。实验结果表明，与其他传统方法和深度学习方法相比，我们的方法在定性和定量评估方面可以有效抑制噪声并保持视网膜结构。我们提出的方法在减少斑点噪声方面表现出良好的性能，并且可以协助进行OCT分析的下游任务。