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

Obtaining fine structures in the whole brain 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 fast, high-resolution whole-brain imaging. We utilized a wide-field microscope and a convolutional neural network for optical sectioning imaging, replacing traditional optical method. 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.