ArticleNature methods2018
High-precision automated reconstruction of neurons with flood-filling networks.
Article in Nature methods, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 168 papers, 1 of them a synthesis that pooled it.
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Who cites it
168 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- Whole organism 3D mapping reveals universal branching topology and biophysical optimization governs vascular and nervous system development.bioRxiv : the preprint server for biology · 2026Article
- Ultrastructure of astrocytes using volume electron microscopy: A scoping review.The Journal of physiology · 2026Article
- Synaptic-resolution connectomics: towards large brains and connectomic screening.Nature reviews. Neuroscience · 2026Review
- A distribution-aware semi-supervised pipeline for cost-effective neuron segmentation.iScience · 2026Article
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108 more citing papers are in PubMed but not listed here.
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10 authors.
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Abstract
Reconstruction of neural circuits from volume electron microscopy data requires the tracing of cells in their entirety, including all their neurites. Automated approaches have been developed for tracing, but their error rates are too high to generate reliable circuit diagrams without extensive human proofreading. We present flood-filling networks, a method for automated segmentation that, similar to most previous efforts, uses convolutional neural networks, but contains in addition a recurrent pathway that allows the iterative optimization and extension of individual neuronal processes. We used flood-filling networks to trace neurons in a dataset obtained by serial block-face electron microscopy of a zebra finch brain. Using our method, we achieved a mean error-free neurite path length of 1.1 mm, and we observed only four mergers in a test set with a path length of 97 mm. The performance of flood-filling networks was an order of magnitude better than that of previous approaches applied to this dataset, although with substantially increased computational costs.
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