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Multi-layered maps of neuropil with segmentation-guided contrastive learning

Dorkenwald, Sven, Li, Peter H., Januszewski, Michał, Berger, Daniel R., Maitin-Shepard, Jeremy, Bodor, Agnes L., Collman, Forrest, Schneider-Mizell, Casey M., da Costa, Nuno Maçarico, Lichtman, Jeff W., Jain, Viren·2023·Nature Methods
DOI 10.1038/s41592-023-02059-8PMC PMC10703674

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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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Metadata

Authors
Dorkenwald, S., Li, P. H., Januszewski, M., Berger, D. R., Maitin-Shepard, J., Bodor, A. L., Collman, F., Schneider-Mizell, C. M., da Costa, N. M., Lichtman, J. W., & Jain, V.
ORCID
Venue
Nature Methods
Publisher
Springer Science and Business Media LLC
Published
2023-11-20
Volume
20
Issue
12
Pages
2011-2020
ISSN
  • 1548-7091
  • 1548-7105
DOI
10.1038/s41592-023-02059-8
PMC
PMC10703674
Cited by
30
References
10
Language
en
Licence
Funders
ODNI | Intelligence Advanced Research Projects Activity (D16PC00004)

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