Compact feature representations for human brain cytoarchitecture using self-supervised learning

The high number of neurons and the complex segregation of the human brain based on cytoarchitecture require an automated analytics approach. Therefore, we are analyzing images of 1 μm resolution histological sections stained for cell bodies using deep learning. The severely limited training data for supervised brain region segmentation represents a challenge for such analysis. We solve it by learning a feature embedding from patches of cortex using a self-supervised Siamese network. The distance between two features corresponds to the distance of the respective image patch locations in the 3D anatomical space. In this contribution, we show that the learned features encode distinctive cytoarchitectonic attributes of the input patches, and form anatomically relevant clusters across the brain.