  Cytoarchitectonic maps provide microstructural reference parcellations of the
brain, describing its organization in terms of the spatial arrangement of
neuronal cell bodies as measured from histological tissue sections. Recent work
provided the first automatic segmentations of cytoarchitectonic areas in the
visual system using Convolutional Neural Networks. We aim to extend this
approach to become applicable to a wider range of brain areas, envisioning a
solution for mapping the complete human brain. Inspired by recent success in
image classification, we propose a contrastive learning objective for encoding
microscopic image patches into robust microstructural features, which are
efficient for cytoarchitectonic area classification. We show that a model
pre-trained using this learning task outperforms a model trained from scratch,
as well as a model pre-trained on a recently proposed auxiliary task. We
perform cluster analysis in the feature space to show that the learned
representations form anatomically meaningful groups.
