ArticleNature methods2024
RoboEM: automated 3D flight tracing for synaptic-resolution connectomics.
Article in Nature methods, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Connectome quality converges predictably to reveal optimal stopping points during proofreading.bioRxiv : the preprint server for biology · 2026Article
- Synaptic-resolution connectomics: towards large brains and connectomic screening.Nature reviews. Neuroscience · 2026Review
- Universal consensus 3D segmentation of cells from 2D segmented stacks.Nature methods · 2025Article
- The emergence of NeuroAI: bridging neuroscience and artificial intelligence.Nature reviews. Neuroscience · 2025Article
- Ultrastructural analysis of synapses after induction of spike-timing-dependent plasticity.Cell reports methods · 2025Article
- CAVE: Connectome Annotation Versioning Engine.Nature methods · 2025Article
- Article
- Comparative prospects of imaging methods for whole-brain mammalian connectomics.Cell reports methods · 2025Review
- Article
- A Novel Semi-automated Proofreading and Mesh Error Detection Pipeline for Neuron Extension.bioRxiv : the preprint server for biology · 2023Article
- CAVE: Connectome Annotation Versioning Engine.bioRxiv : the preprint server for biology · 2023Article
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4 authors.
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Abstract
Mapping neuronal networks from three-dimensional electron microscopy (3D-EM) data still poses substantial reconstruction challenges, in particular for thin axons. Currently available automated image segmentation methods require manual proofreading for many types of connectomic analysis. Here we introduce RoboEM, an artificial intelligence-based self-steering 3D 'flight' system trained to navigate along neurites using only 3D-EM data as input. Applied to 3D-EM data from mouse and human cortex, RoboEM substantially improves automated state-of-the-art segmentations and can replace manual proofreading for more complex connectomic analysis problems, yielding computational annotation cost for cortical connectomes about 400-fold lower than the cost of manual error correction.
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