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

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. S

SUBMITTER: Dorkenwald S 

PROVIDER: S-EPMC10703674 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature