  Cytoarchitectonic parcellations of the human brain serve as anatomical
references in multimodal atlas frameworks. They are based on analysis of
cell-body stained histological sections and the identification of borders
between brain areas. The de-facto standard involves a semi-automatic,
reproducible border detection, but does not scale with high-throughput imaging
in large series of sections at microscopical resolution. Automatic
parcellation, however, is extremely challenging due to high variation in the
data, and the need for a large field of view at microscopic resolution. The
performance of a recently proposed Convolutional Neural Network model that
addresses this problem especially suffers from the naturally limited amount of
expert annotations for training. To circumvent this limitation, we propose to
pre-train neural networks on a self-supervised auxiliary task, predicting the
3D distance between two patches sampled from the same brain. Compared to a
random initialization, fine-tuning from these networks results in significantly
better segmentations. We show that the self-supervised model has implicitly
learned to distinguish several cortical brain areas -- a strong indicator that
the proposed auxiliary task is appropriate for cytoarchitectonic mapping.
