目的:光学相干断层扫描(OCT)因其无创性,高分辨率成像能力。然而,其低相干性原理固有的斑点噪声会降低图像质量并损害诊断准确性。虽然深度学习方法在减少斑点噪声方面表现出了希望,获得良好配准的图像对仍然具有挑战性,导致不成对方法的发展。尽管有潜力,现有的不成对方法遭受网络结构或交互机制的冗余。因此,非配对OCT去噪的更简化方法至关重要.
方法:在这项工作中,我们提出了一种新颖的非配对OCT图像去噪方法,称为噪声模拟学习(NIL)。NIL包括三个主要模块:噪声提取模块,通过对噪声图像进行去噪提取噪声特征;噪声模仿模块,合成嘈杂的图像并生成虚假的干净图像;以及对抗性学习模块,通过对抗性训练区分真实和假的干净图像。NIL的复杂度明显低于以前的未配对方法,只利用一个发生器和一个鉴别器进行训练。
结果:通过有效地融合不成对的图像并采用对抗训练,NIL可以提取更多的斑点噪声信息,以增强去噪性能。建立在NIL上,我们提出了一种OCT图像去噪管道,NIL-NAFNet。这条管道实现了PSNR,SSIM,和RMSE值31.27dB,分别为0.865和7.00,在PKU37数据集上。大量实验表明,我们的方法在定性和定量上都优于最先进的不成对方法。
结论:这些发现表明,所提出的NIL是一种简单而有效的方法,用于减少不成对的OCT斑点噪声。基于NIL的OCT去噪管道展示了卓越的性能和效率。通过解决斑点噪声,而不需要良好配准的图像对,该方法可以提高图像质量和临床诊断的准确性。
OBJECTIVE: Optical coherence tomography (OCT) is widely used in clinical practice for its non-invasive, high-resolution imaging capabilities. However, speckle noise inherent to its low coherence principle can degrade image quality and compromise diagnostic accuracy. While deep learning methods have shown promise in reducing speckle noise, obtaining well-registered image pairs remains challenging, leading to the development of unpaired methods. Despite their potential, existing unpaired methods suffer from redundancy in network structures or interaction mechanisms. Therefore, a more streamlined method for unpaired OCT denoising is essential.
METHODS: In this work, we propose a novel unpaired method for OCT image denoising, referred to as noise-imitation learning (NIL). NIL comprises three primary modules: the noise extraction module, which extracts noise features by denoising noisy images; the noise imitation module, which synthesizes noisy images and generates fake clean images; and the adversarial learning module, which differentiates between real and fake clean images through adversarial training. The complexity of NIL is significantly lower than that of previous unpaired methods, utilizing only one generator and one discriminator for training.
RESULTS: By efficiently fusing unpaired images and employing adversarial training, NIL can extract more speckle noise information to enhance denoising performance. Building on NIL, we propose an OCT image denoising pipeline, NIL-NAFNet. This pipeline achieved PSNR, SSIM, and RMSE values of 31.27 dB, 0.865, and 7.00, respectively, on the PKU37 dataset. Extensive experiments suggest that our method outperforms state-of-the-art unpaired methods both qualitatively and quantitatively.
CONCLUSIONS: These findings indicate that the proposed NIL is a simple yet effective method for unpaired OCT speckle noise reduction. The OCT denoising pipeline based on NIL demonstrates exceptional performance and efficiency. By addressing speckle noise without requiring well-registered image pairs, this method can enhance image quality and diagnostic accuracy in clinical practice.