Deep-learning-based motion-correction algorithm in optical resolution photoacoustic microscopy
Deep-learning-based motion-correction algorithm in optical resolution photoacoustic microscopy
复制标题
光学分辨率光声显微镜中基于深度学习的运动校正算法
DOI：
10.1186/s42492-019-0022-9
复制
发表时间：
2019-10-29
影响因子：
2.8
通讯作者：
Xi, Lei
中科院分区：
文献类型：
--
作者：
Chen, Xingxing;Qi, Weizhi;Xi, Lei
在这项研究中，我们提出了一种基于深度学习的方法来校正光学分辨率光声显微镜（OR-PAM）中的运动伪影。该方法是一个卷积神经网络，从带有运动伪影的输入原始数据到输出校正图像建立端到端映射。首先，我们进行了仿真研究，以评估所提出方法的可行性和有效性。其次，我们将该方法用于处理具有多个运动伪影的大鼠脑血管图像，以评估其在体内应用的性能。结果表明，该方法对大血管和毛细血管网络都有很好的效果。与传统方法相比，本文提出的方法可以通过修改训练集，方便地满足OR-PAM中不同场景的运动修正。
In this study, we propose a deep-learning-based method to correct motion artifacts in optical resolution photoacoustic microscopy (OR-PAM). The method is a convolutional neural network that establishes an end-to-end map from input raw data with motion artifacts to output corrected images. First, we performed simulation studies to evaluate the feasibility and effectiveness of the proposed method. Second, we employed this method to process images of rat brain vessels with multiple motion artifacts to evaluate its performance for in vivo applications. The results demonstrate that this method works well for both large blood vessels and capillary networks. In comparison with traditional methods, the proposed method in this study can be easily modified to satisfy different scenarios of motion corrections in OR-PAM by revising the training sets.