ArticleBiomedical optics express2020
Deep-learning-based whole-brain imaging at single-neuron resolution.
Article in Biomedical optics express, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
9 citing papers in PubMed.
- Brain-SAM: a general automatic SAM-based segmentation model for brain science images.Biomedical optics express · 2026Article
- Image enhancement network based on fiber topology-preserving multi-module fusion for neuron reconstruction.Biomedical optics express · 2025Article
- Whole-brain Optical Imaging: A Powerful Tool for Precise Brain Mapping at the Mesoscopic Level.Neuroscience bulletin · 2023Review
- Comprehensive deep learning model for 3D color holography.Scientific reports · 2022Article
- Deep-3D microscope: 3D volumetric microscopy of thick scattering samples using a wide-field microscope and machine learning.Biomedical optics express · 2022Article
- Multicolor high-resolution whole-brain imaging for acquiring and comparing the brain-wide distributions of type-specific and projection-specific neurons with anatomical annotation in the same brain.Frontiers in neuroscience · 2022Article
- Deep learning 2D and 3D optical sectioning microscopy using cross-modality Pix2Pix cGAN image translation.Biomedical optics express · 2021Article
- Neuron tracing and quantitative analyses of dendritic architecture reveal symmetrical three-way-junctions and phenotypes of git-1 in C. elegans.PLoS computational biology · 2021Article
- Seeing the Forest and Its Trees Together: Implementing 3D Light Microscopy Pipelines for Cell Type Mapping in the Mouse Brain.Frontiers in neuroanatomy · 2021Review
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.