Ning Kefu, Zhang Xiaoyu, Gao Xuefei, Jiang Tao, Wang He, Chen Siqi, Li Anan, Yuan Jing
Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Innovation Institute, Huazhong University of Science and Technology, Wuhan 430074, China.
These authors contributed equally to this work.
Biomed Opt Express. 2020 Jun 8;11(7):3567-3584. doi: 10.1364/BOE.393081. eCollection 2020 Jul 1.
Obtaining fine structures of neurons is necessary for understanding brain function. Simple and effective methods for large-scale 3D imaging at optical resolution are still lacking. Here, we proposed a deep-learning-based fluorescence micro-optical sectioning tomography (DL-fMOST) method for high-throughput, high-resolution whole-brain imaging. We utilized a wide-field microscope for imaging, a U-net convolutional neural network for real-time optical sectioning, and histological sectioning for exceeding the imaging depth limit. A 3D dataset of a mouse brain with a voxel size of 0.32 × 0.32 × 2 µm was acquired in 1.5 days. We demonstrated the robustness of DL-fMOST for mouse brains with labeling of different types of neurons.
获取神经元的精细结构对于理解大脑功能至关重要。目前仍缺乏在光学分辨率下进行大规模三维成像的简单有效方法。在此，我们提出了一种基于深度学习的荧光显微光学切片断层成像（DL-fMOST）方法，用于高通量、高分辨率的全脑成像。我们利用宽场显微镜进行成像，使用U-net卷积神经网络进行实时光学切片，并结合组织学切片以突破成像深度限制。在1.5天内获取了体素大小为0.32×0.32×2 µm的小鼠脑三维数据集。我们通过对不同类型神经元进行标记，证明了DL-fMOST对小鼠脑成像的稳健性。
