ArticlePLoS computational biology2024
DeepD3, an open framework for automated quantification of dendritic spines.
Article in PLoS computational biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Deep learning-assisted mapping of dendritic spines using sequential 2D two-photon calcium imaging.iScience · 2026Article
- Complementary roles of cell-type-specific plasticity in shaping neocortical dynamics for learning action timing.Nature communications · 2026Article
- SynAPSeg: A novel dataset and image analysis framework for deep learning-based synapse detection and quantification.PLoS computational biology · 2026Article
- SynAPSeg: A novel dataset and image analysis framework for deep learning-based synapse detection and quantification.bioRxiv : the preprint server for biology · 2026Article
- GABAeNeuro · 2026Article
- Article
- A deep learning pipeline for accurate and automated restoration, segmentation, and quantification of dendritic spines.Cell reports methods · 2025Article
- SpyDen: simplifying molecular and structural analysis across spines and dendrites.Bioinformatics (Oxford, England) · 2025Article
- 2dSpAn-Auto: an automated tool for analysis of two-dimensional dendritic spine images.BMC bioinformatics · 2025Article
- Structural neural plasticity evoked by rapid-acting antidepressant interventions.Nature reviews. Neuroscience · 2025Review
- Super-resolution microscopy and deep learning methods: what can they bring to neuroscience: from neuron to 3D spine segmentation.Frontiers in neuroinformatics · 2025Review
- Cerebral Hypoxia-Induced Molecular Alterations and Their Impact on the Physiology of Neurons and Dendritic Spines: A Comprehensive Review.Cellular and molecular neurobiology · 2024Review
- Nanoscale organization is changed in native, surface AMPARs by mouse brain region and tauopathy.bioRxiv : the preprint server for biology · 2024Article
- Implicit neural representations in light microscopy.Biomedical optics express · 2024Article
- Neuroligin-1 dependent phosphotyrosine signaling in excitatory synapse differentiation.Frontiers in molecular neuroscience · 2024Article
- Utilizing 2D-region-based CNNs for automatic dendritic spine detection in 3D live cell imaging.Scientific reports · 2023Article
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Authors and funding
4 authors.
Funding
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Abstract
Dendritic spines are the seat of most excitatory synapses in the brain, and a cellular structure considered central to learning, memory, and activity-dependent plasticity. The quantification of dendritic spines from light microscopy data is usually performed by humans in a painstaking and error-prone process. We found that human-to-human variability is substantial (inter-rater reliability 82.2±6.4%), raising concerns about the reproducibility of experiments and the validity of using human-annotated 'ground truth' as an evaluation method for computational approaches of spine identification. To address this, we present DeepD3, an open deep learning-based framework to robustly quantify dendritic spines in microscopy data in a fully automated fashion. DeepD3's neural networks have been trained on data from different sources and experimental conditions, annotated and segmented by multiple experts and they offer precise quantification of dendrites and dendritic spines. Importantly, these networks were validated in a number of datasets on varying acquisition modalities, species, anatomical locations and fluorescent indicators. The entire DeepD3 open framework, including the fully segmented training data, a benchmark that multiple experts have annotated, and the DeepD3 model zoo is fully available, addressing the lack of openly available datasets of dendritic spines while offering a ready-to-use, flexible, transparent, and reproducible spine quantification method.
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