Jin Yuncheng, Chen Jiajia, Wu Chenxue, Chen Zhihong, Zhang XIngyu, Shen Hui-Liang, Gong Wei, Si Ke
Opt Express. 2020 Jul 6;28(14):20738-20747. doi: 10.1364/OE.396321.
The application of machine learning in wavefront reconstruction has brought great benefits to real-time, non-invasive, deep tissue imaging in biomedical research. However, due to the diversity and heterogeneity of biological tissues, it is difficult to train the dataset with a unified model. In general, the utilization of some unified models will result in the specific sample falling outside the training set, leading to low accuracy of the machine learning model in some real applications. This paper proposes a sensorless wavefront reconstruction method based on transfer learning to overcome the domain shift introduced by the difference between the training set and the target test set. We build a weights-sharing two-stream convolutional neural network (CNN) framework for the prediction of Zernike coefficient, in which a large number of labeled randomly generated samples serve as the source-domain data and the unlabeled specific samples serve as the target-domain data at the same time. By training on massive labeled simulated data with domain adaptation to unlabeled target-domain data, the network shows better performance on the target tissue samples. Experimental results show that the accuracy of the proposed method is 18.5% higher than that of conventional CNN-based method and the peak intensities of the point spread function (PSF) are more than 20% higher with almost the same training time and processing time. The better compensation performance on target sample could have more advantages when handling complex aberrations, especially the aberrations caused by various histological characteristics, such as refractive index inhomogeneity and biological motion in biological tissues.
机器学习在波前重建中的应用为生物医学研究中的实时、非侵入性、深层组织成像带来了巨大益处。然而，由于生物组织的多样性和异质性，难以用统一模型训练数据集。一般来说，使用一些统一模型会导致特定样本落在训练集之外，从而使机器学习模型在某些实际应用中的准确性较低。本文提出一种基于迁移学习的无传感器波前重建方法，以克服训练集与目标测试集差异所引入的域偏移。我们构建了一个用于预测泽尼克系数的权重共享双流卷积神经网络（CNN）框架，其中大量随机生成的带标签样本同时作为源域数据，未带标签的特定样本作为目标域数据。通过对大量带标签的模拟数据进行训练，并对未带标签的目标域数据进行域适应，该网络在目标组织样本上表现出更好的性能。实验结果表明，所提方法的准确率比传统基于CNN的方法高18.5%，点扩散函数（PSF）的峰值强度在训练时间和处理时间几乎相同的情况下高出20%以上。在处理复杂像差时，尤其是由各种组织学特征（如生物组织中的折射率不均匀性和生物运动）引起的像差时，对目标样本更好的补偿性能可能具有更多优势。
