Revealing Cortical Layers In Histological Brain Images With Self-Supervised Graph Convolutional Networks Applied To Cell-Graphs
This work addresses the challenge of manual cortical layer delineation for neuroanatomists, offering a novel self-supervised approach that is incremental in automating cross-species brain structure studies.
The paper tackles the problem of identifying cerebral cortex layers in histological brain images without extensive annotated datasets by introducing a self-supervised graph convolutional network applied to cell-graphs, resulting in a method that sidesteps annotation needs and accelerates cytoarchitecture analyses.
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.