ArticleFrontiers in neuroinformatics2019
DeepBouton: Automated Identification of Single-Neuron Axonal Boutons at the Brain-Wide Scale.
Article in Frontiers in neuroinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed.
- A Multi-Scale Neuron Morphometry Dataset from Peta-voxel Mouse Whole-Brain Images.Scientific data · 2025Article
- Neuronal diversity and stereotypy at multiple scales through whole brain morphometry.Nature communications · 2024Article
- Microglia regulate sleep through calcium-dependent modulation of norepinephrine transmission.Nature neuroscience · 2024Article
- Excitatory nucleo-olivary pathway shapes cerebellar outputs for motor control.Nature neuroscience · 2023Article
- Approaches to quantify axonal morphology for the analysis of axonal degeneration.Neural regeneration research · 2023Article
- BoutonNet: an automatic method to detect anterogradely labeled presynaptic boutons in brain tissue sections.Brain structure & function · 2022Article
- Petabyte-Scale Multi-Morphometry of Single Neurons for Whole Brains.Neuroinformatics · 2022Article
- Niwaki Instead of Random Forests: Targeted Serial Sectioning Scanning Electron Microscopy With Reimaging Capabilities for Exploring Central Nervous System Cell Biology and Pathology.Frontiers in neuroanatomy · 2021Review
- Skeleton optimization of neuronal morphology based on three-dimensional shape restrictions.BMC bioinformatics · 2020Article
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12 authors.
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Abstract
Fine morphological reconstruction of individual neurons across the entire brain is essential for mapping brain circuits. Inference of presynaptic axonal boutons, as a key part of single-neuron fine reconstruction, is critical for interpreting the patterns of neural circuit wiring schemes. However, automated bouton identification remains challenging for current neuron reconstruction tools, as they focus mainly on neurite skeleton drawing and have difficulties accurately quantifying bouton morphology. Here, we developed an automated method for recognizing single-neuron axonal boutons in whole-brain fluorescence microscopy datasets. The method is based on deep convolutional neural networks and density-peak clustering. High-dimensional feature representations of bouton morphology can be learned adaptively through convolutional networks and used for bouton recognition and subtype classification. We demonstrate that the approach is effective for detecting single-neuron boutons at the brain-wide scale for both long-range pyramidal projection neurons and local interneurons.
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