ArticleBrain informatics2018
DeepNeuron: an open deep learning toolbox for neuron tracing.
Article in Brain informatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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Who cites it
33 citing papers in PubMed.
- Automated Proofreading of Digitally Reconstructed Neural Morphology Enhances Accuracy, Scalability, and Standardization.bioRxiv : the preprint server for biology · 2026Article
- Automatic and accurate reconstruction of long-range axonal projections of single-neuron in mouse brain.eLife · 2025Article
- Gapr for large-scale collaborative single-neuron reconstruction.Nature methods · 2024Article
- High-throughput analysis of dendrite and axonal arbors reveals transcriptomic correlates of neuroanatomy.Nature communications · 2024Article
- Connecto-informatics at the mesoscale: current advances in image processing and analysis for mapping the brain connectivity.Brain informatics · 2024Review
- DDeep3M+: adaptive enhancement powered weakly supervised learning for neuron segmentation.Neurophotonics · 2023Article
- RGC-Net: An Automatic Reconstruction and Quantification Algorithm for Retinal Ganglion Cells Based on Deep Learning.Translational vision science & technology · 2023Article
- NeuRegenerate: A Framework for Visualizing Neurodegeneration.IEEE transactions on visualization and computer graphics · 2023Article
- An unbiased, automated platform for scoring dopaminergic neurodegeneration inbioRxiv : the preprint server for biology · 2023Article
- An unbiased, automated platform for scoring dopaminergic neurodegeneration in C. elegans.PloS one · 2023Article
- Tracing weak neuron fibers.Bioinformatics (Oxford, England) · 2023Article
- Neuron tracing from light microscopy images: automation, deep learning and bench testing.Bioinformatics (Oxford, England) · 2022Review
- NeuroConstruct: 3D Reconstruction and Visualization of Neurites in Optical Microscopy Brain Images.IEEE transactions on visualization and computer graphics · 2022Article
- Practical guide for preparation, computational reconstruction and analysis of 3D human neuronal networks in control and ischaemic conditions.Development (Cambridge, England) · 2022Article
- Smart imaging to empower brain-wide neuroscience at single-cell levels.Brain informatics · 2022Review
- Bringing machine learning to research on intellectual and developmental disabilities: taking inspiration from neurological diseases.Journal of neurodevelopmental disorders · 2022Review
- Hidden Markov modeling for maximum probability neuron reconstruction.Communications biology · 2022Article
- Classifying the tracing difficulty of 3D neuron image blocks based on deep learning.Brain informatics · 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
- Improving scalability in systems neuroscience.Neuron · 2021Review
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4 authors.
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Abstract
Reconstructing three-dimensional (3D) morphology of neurons is essential for understanding brain structures and functions. Over the past decades, a number of neuron tracing tools including manual, semiautomatic, and fully automatic approaches have been developed to extract and analyze 3D neuronal structures. Nevertheless, most of them were developed based on coding certain rules to extract and connect structural components of a neuron, showing limited performance on complicated neuron morphology. Recently, deep learning outperforms many other machine learning methods in a wide range of image analysis and computer vision tasks. Here we developed a new Open Source toolbox, DeepNeuron, which uses deep learning networks to learn features and rules from data and trace neuron morphology in light microscopy images. DeepNeuron provides a family of modules to solve basic yet challenging problems in neuron tracing. These problems include but not limited to: (1) detecting neuron signal under different image conditions, (2) connecting neuronal signals into tree(s), (3) pruning and refining tree morphology, (4) quantifying the quality of morphology, and (5) classifying dendrites and axons in real time. We have tested DeepNeuron using light microscopy images including bright-field and confocal images of human and mouse brain, on which DeepNeuron demonstrates robustness and accuracy in neuron tracing.
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