ArticleNature methods2023
Multi-layered maps of neuropil with segmentation-guided contrastive learning.
Article in Nature methods, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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
18 citing papers in PubMed, 27 citations in OpenAlex.
- Synaptic Organization of the Primate Retina: Toward a Human Foveal Connectome.Annual review of vision science · 2026Review
- The synaptic architecture of layer 5 thick tufted excitatory neurons in mouse visual cortex.Nature neuroscience · 2025Article
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- CAVE: Connectome Annotation Versioning Engine.Nature methods · 2025Article
- In vivo cell-type and brain region classification via multimodal contrastive learning.bioRxiv : the preprint server for biology · 2025Article
- Robust semi-automatic vessel tracing in the human retinal image by an instance segmentation neural network.Science advances · 2025Article
- Article
- Article
- CODI: Enhancing machine learning-based molecular profiling through contextual out-of-distribution integration.PNAS nexus · 2024Article
- Multiplexed volumetric CLEM enabled by scFvs provides insights into the cytology of cerebellar cortex.Nature communications · 2024Article
- Next-generation AI for connectomics.Nature methods · 2024Article
- A petavoxel fragment of human cerebral cortex reconstructed at nanoscale resolution.Science (New York, N.Y.) · 2024Article
- Cell-type-specific inhibitory circuitry from a connectomic census of mouse visual cortex.bioRxiv : the preprint server for biology · 2024Article
- Article
- CAVE: Connectome Annotation Versioning Engine.bioRxiv : the preprint server for biology · 2023Article
- Multiplexed volumetric CLEM enabled by antibody derivatives provides new insights into the cytology of the mouse cerebellar cortex.Research square · 2023Article
- A Convenient All-Cell Optical Imaging Method Compatible with Serial SEM for Brain Mapping.Brain sciences · 2023Article
- SyConn2: dense synaptic connectivity inference for volume electron microscopy.Nature methods · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors at 3 institutions in 2 countries.
Funding
Abstract
Maps of the nervous system that identify individual cells along with their type, subcellular components and connectivity have the potential to elucidate fundamental organizational principles of neural circuits. Nanometer-resolution imaging of brain tissue provides the necessary raw data, but inferring cellular and subcellular annotation layers is challenging. We present segmentation-guided contrastive learning of representations (SegCLR), a self-supervised machine learning technique that produces representations of cells directly from 3D imagery and segmentations. When applied to volumes of human and mouse cortex, SegCLR enables accurate classification of cellular subcompartments and achieves performance equivalent to a supervised approach while requiring 400-fold fewer labeled examples. SegCLR also enables inference of cell types from fragments as small as 10 μm, which enhances the utility of volumes in which many neurites are truncated at boundaries. Finally, SegCLR enables exploration of layer 5 pyramidal cell subtypes and automated large-scale analysis of synaptic partners in mouse visual cortex.
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What OpenQuestion holds
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 OpenQuestion graph.