Open AccessDeep-learning-based whole-brain imaging at single-neuron resolution
Author(s) -
Kefu Ning,
Xiaoyu Zhang,
Xuefei Gao,
Tao Jiang,
He Wang,
Siqi Chen,
Anan Li,
Jing Yuan
Publication year - 2020
Publication title -
biomedical optics express
Language(s) - Uncategorized
Resource type - Journals
SCImago Journal Rank - 1.362
H-Index - 86
ISSN - 2156-7085
DOI - 10.1364/boe.393081
Subject(s) - optical sectioning , microscopy , convolutional neural network , neuroimaging , light sheet fluorescence microscopy , voxel , optical imaging , image resolution , artificial intelligence , fluorescence lifetime imaging microscopy , deep learning , superresolution , computer science , optics , fluorescence , neuroscience , physics , biology , scanning confocal electron microscopy , image (mathematics)
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.