Presentation
5 March 2021 Deep-learning-based whole-brain imaging at single-neuron resolution
Author Affiliations +
Abstract
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.
Conference Presentation
© (2021) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Kefu Ning, Xiaoyu Zhang, Xuefei Gao, Tao Jiang, He Wang, Siqi Chen, Anan Li, and Jing Yuan "Deep-learning-based whole-brain imaging at single-neuron resolution", Proc. SPIE 11629, Optical Techniques in Neurosurgery, Neurophotonics, and Optogenetics, 1162928 (5 March 2021); https://doi.org/10.1117/12.2582870
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KEYWORDS
Image resolution

Brain

Neurons

Microscopes

Neuroimaging

Neuroscience

Optical imaging

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