ArticleNature communications2019
Learning cellular morphology with neural networks.
Article in Nature communications, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- An anatomical substrate of credit assignment in reinforcement learning.bioRxiv : the preprint server for biology · 2025Article
- Large-scale foundation models and generative AI for BigData neuroscience.Neuroscience research · 2025Review
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- Efficient cell-wide mapping of mitochondria in electron microscopic volumes using webKnossos.Cell reports methods · 2025Article
- Learning meaningful representation of single-neuron morphology via large-scale pre-training.Bioinformatics (Oxford, England) · 2024Article
- Morphological entropy encodes cellular migration strategies on multiple length scales.NPJ systems biology and applications · 2024Article
- Multi-gate Weighted Fusion Network for neuronal morphology classification.Frontiers in neuroscience · 2024Article
- Current Progress and Challenges in Large-Scale 3D Mitochondria Instance Segmentation.IEEE transactions on medical imaging · 2023Article
- Multi-layered maps of neuropil with segmentation-guided contrastive learning.Nature methods · 2023Article
- A robust approach to 3D neuron shape representation for quantification and classification.BMC bioinformatics · 2023Article
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- Approaches to quantify axonal morphology for the analysis of axonal degeneration.Neural regeneration research · 2023Article
- Virtual Fly Brain-An interactive atlas of theFrontiers in physiology · 2023Article
- Mapping of the zebrafish brain takes shape.Nature methods · 2022Article
- SyConn2: dense synaptic connectivity inference for volume electron microscopy.Nature methods · 2022Article
- Article
- Article
- Learning and Segmenting Dense Voxel Embeddings for 3D Neuron Reconstruction.IEEE transactions on medical imaging · 2021Article
- Prediction of RNA subcellular localization: Learning from heterogeneous data sources.iScience · 2021Review
- NuMorph: Tools for cortical cellular phenotyping in tissue-cleared whole-brain images.Cell reports · 2021Article
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Authors and funding
5 authors.
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Abstract
Reconstruction and annotation of volume electron microscopy data sets of brain tissue is challenging but can reveal invaluable information about neuronal circuits. Significant progress has recently been made in automated neuron reconstruction as well as automated detection of synapses. However, methods for automating the morphological analysis of nanometer-resolution reconstructions are less established, despite the diversity of possible applications. Here, we introduce cellular morphology neural networks (CMNs), based on multi-view projections sampled from automatically reconstructed cellular fragments of arbitrary size and shape. Using unsupervised training, we infer morphology embeddings (Neuron2vec) of neuron reconstructions and train CMNs to identify glia cells in a supervised classification paradigm, which are then used to resolve neuron reconstruction errors. Finally, we demonstrate that CMNs can be used to identify subcellular compartments and the cell types of neuron reconstructions.
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