ArticleThe Journal of cell biology2025
CryoVesNet: A dedicated framework for synaptic vesicle segmentation in cryo-electron tomograms.
Article in The Journal of cell biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed.
- MemBrain v2: an end-to-end tool for the analysis of membranes in cryo-electron tomography.Nature methods · 2026Article
- Vesicle3D: An Integrative Platform for 3D Segmentation and Analysis of Vesicles in Cryo-electron Tomograms.Neuroscience bulletin · 2026Article
- VesiclePy: A machine learning vesicle analysis toolbox for volume electron microscopy.PLoS computational biology · 2026Article
- MVGFormer: Multi-view perspective with graph-guided transformer for cryo-ET segmentation.Knowledge-based systems · 2026Article
- SynapseNet: Deep learning for automatic synapse reconstruction.Molecular biology of the cell · 2025Article
- The synaptic vesicle cluster as a controller of pre- and postsynaptic structure and function.The Journal of physiology · 2025Review
- A hybrid YOLO-UNet3D framework for automated protein particle annotation in Cryo-ET images.Scientific reports · 2025Article
- The postsynaptic density in excitatory synapses is composed of clustered, heterogeneous nanoblocks.The Journal of cell biology · 2025Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Cryo-electron tomography (cryo-ET) has the potential to reveal cell structure down to atomic resolution. Nevertheless, cellular cryo-ET data is highly complex, requiring image segmentation for visualization and quantification of subcellular structures. Due to noise and anisotropic resolution in cryo-ET data, automatic segmentation based on classical computer vision approaches usually does not perform satisfactorily. Communication between neurons relies on neurotransmitter-filled synaptic vesicle (SV) exocytosis. Cryo-ET study of the spatial organization of SVs and their interconnections allows a better understanding of the mechanisms of exocytosis regulation. Accurate SV segmentation is a prerequisite to obtaining a faithful connectivity representation. Hundreds of SVs are present in a synapse, and their manual segmentation is a bottleneck. We addressed this by designing a workflow consisting of a convolutional network followed by post-processing steps. Alongside, we provide an interactive tool for accurately segmenting spherical vesicles. Our pipeline can in principle segment spherical vesicles in any cell type as well as extracellular and in vitro spherical vesicles.
Indexed as
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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.