  Identifying cerebral cortex layers is crucial for comparative studies of the
cytoarchitecture aiming at providing insights into the relations between brain
structure and function across species. The absence of extensive annotated
datasets typically limits the adoption of machine learning approaches, leading
to the manual delineation of cortical layers by neuroanatomists. We introduce a
self-supervised approach to detect layers in 2D Nissl-stained histological
slices of the cerebral cortex. It starts with the segmentation of individual
cells and the creation of an attributed cell-graph. A self-supervised graph
convolutional network generates cell embeddings that encode morphological and
structural traits of the cellular environment and are exploited by a community
detection algorithm for the final layering. Our method, the first
self-supervised of its kind with no spatial transcriptomics data involved,
holds the potential to accelerate cytoarchitecture analyses, sidestepping
annotation needs and advancing cross-species investigation.
