Appl Opt. 2020 Feb 10;59(5):1461-1470. doi: 10.1364/AO.377810.
Deep learning has been actively investigated for various applications such as image classification, computer vision, and regression tasks, and it has shown state-of-the-art performance. In diffuse optical tomography (DOT), the accurate estimation of the bulk optical properties of a medium is paramount because it directly affects the overall image quality. In this work, we exploit deep learning to propose a novel, to the best of our knowledge, convolutional neural network (CNN)-based approach to estimate the bulk optical properties of a highly scattering medium such as biological tissue in DOT. We validated the proposed method by using experimental, as well as, simulated data. For performance assessment, we compared the results of the proposed method with those of existing approaches. The results demonstrate that the proposed CNN-based approach for bulk optical property estimation outperforms existing methods in terms of estimation accuracy, with lower computation time.
深度学习在图像分类、计算机视觉和回归任务等各种应用中得到了积极的研究，并展示了最先进的性能。在漫射光学断层扫描（DOT）中，准确估计介质的体光学性质至关重要，因为它直接影响整体图像质量。在这项工作中，我们利用深度学习提出了一种新颖的、据我们所知基于卷积神经网络（CNN）的方法，用于估计 DOT 中生物组织等高度散射介质的体光学性质。我们使用实验和模拟数据验证了所提出的方法。为了进行性能评估，我们将所提出方法的结果与现有方法的结果进行了比较。结果表明，所提出的基于 CNN 的体光学性质估计方法在估计准确性方面优于现有方法，并且计算时间更短。
