ArticleMolecular biology of the cell2025
SynapseNet: Deep learning for automatic synapse reconstruction.
Article in Molecular biology of the cell, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- CoCryoViS: Collaborative online cryo-electron tomography visualization system.Protein science : a publication of the Protein Society · 2026Article
- A versatile nanobody platform for live and super-resolution imaging of synaptic vesicle dynamics and plasticity in rodent and human neurons.Journal of nanobiotechnology · 2026Article
- VesiclePy: A machine learning vesicle analysis toolbox for volume electron microscopy.PLoS computational biology · 2026Article
- Encephalopathy-linked UFM1 variants impede neuronal protein translation, development, and function.EMBO molecular medicine · 2026Article
- Structure and function of otoferlin, a synaptic protein of sensory hair cells essential for hearing.Science advances · 2025Article
- Neddylation regulates the development and function of glutamatergic neurons.Communications biology · 2025Article
- The synaptic organization of the human temporal lobe neocortex by high-resolution transmission, focused ion beam scanning, and electron microscopic tomography.Anatomical science international · 2025Review
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22 authors.
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Abstract
Electron microscopy is an important technique for the study of synaptic morphology and its relation to synaptic function. The data analysis for this task requires the segmentation of the relevant synaptic structures, such as synaptic vesicles (SV), active zones, mitochondria, presynaptic densities, synaptic ribbons, and synaptic compartments. Previous studies were predominantly based on manual segmentation, which is very time-consuming and prevented the systematic analysis of large datasets. Here, we introduce SynapseNet, a tool for the automatic segmentation and analysis of synapses in electron micrographs. It can reliably segment SVs and other synaptic structures in a wide range of electron microscopy approaches, thanks to a large annotated dataset, which we assembled, and domain adaptation functionality we developed. We demonstrated its capability for (semi-)automatic biological analysis in two applications and made it available as an easy-to-use tool to enable novel data-driven insights into synapse organization and function.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.