Deep-learning-based whole-brain imaging at single-neuron resolution

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 x 0.32 x 2 mu m was acquired in 1.5 days. We demonstrated the robustness of DL-fMOST for mouse brains with labeling of different types of neurons. (C) 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement