Yuan Zhuoqun, Yang Di, Zhao Jingzhu, Liang Yanmei
Institute of Modern Optics, Nankai University, Tianjin Key Laboratory of Micro-scale Optical Information Science and Technology, Tianjin 300350, People's Republic of China.
Department of Thyroid and Neck Tumor, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin 300060, People's Republic of China.
Phys Med Biol. 2024 May 30;69(11). doi: 10.1088/1361-6560/ad4c52.
. The quality of optical coherence tomography (OCT)images is crucial for clinical visualization of early disease. As a three dimensional and coherent imaging, defocus and speckle noise are inevitable, which seriously affect evaluation of microstructure of bio-samples in OCT images. The deep learning has demonstrated great potential in OCT refocusing and denoising, but it is limited by the difficulty of sufficient paired training data. This work aims to develop an unsupervised method to enhance the quality of OCTimages.. We proposed an unsupervised deep learning-based pipeline. The unregistered defocused conventional OCT images and focused speckle-free OCT images were collected by a home-made speckle modulating OCT system to construct the dataset. The image enhancement model was trained with the cycle training strategy. Finally, the speckle noise and defocus were both effectively improved.. The experimental results on complex bio-samples indicated that the proposed method is effective and generalized in enhancing the quality of OCTimages.. The proposed unsupervised deep learning method helps to reduce the complexity of data construction, which is conducive to practical applications in OCT bio-sample imaging.
光学相干断层扫描（OCT）图像的质量对于疾病早期的临床可视化至关重要。作为一种三维相干成像，散焦和散斑噪声不可避免，这严重影响了对OCT图像中生物样本微观结构的评估。深度学习在OCT重聚焦和去噪方面已显示出巨大潜力，但受到充足配对训练数据获取困难的限制。这项工作旨在开发一种无监督方法来提高OCT图像的质量。我们提出了一种基于无监督深度学习的流程。通过自制的散斑调制OCT系统收集未配准的散焦传统OCT图像和聚焦无散斑的OCT图像，以构建数据集。使用循环训练策略训练图像增强模型。最后，散斑噪声和散焦都得到了有效改善。在复杂生物样本上的实验结果表明，所提出的方法在提高OCT图像质量方面是有效且通用的。所提出的无监督深度学习方法有助于降低数据构建的复杂性，有利于在OCT生物样本成像中的实际应用。
