Analyzing Regional Organization of the Human Hippocampus in 3D-PLI Using
  Contrastive Learning and Geometric Unfolding
Alexander Oberstrass Jordan DeKraker Nicola Palomero-Gallagher Sascha E. A. Muenzing Alan C. Evans Markus Axer Katrin Amunts Timo Dickscheid
Understanding the cortical organization of the human brain requires
interpretable descriptors for distinct structural and functional imaging data.
3D polarized light imaging (3D-PLI) is an imaging modality for visualizing
fiber architecture in postmortem brains with high resolution that also captures
the presence of cell bodies, for example, to identify hippocampal subfields.
The rich texture in 3D-PLI images, however, makes this modality particularly
difficult to analyze and best practices for characterizing architectonic
patterns still need to be established. In this work, we demonstrate a novel
method to analyze the regional organization of the human hippocampus in 3D-PLI
by combining recent advances in unfolding methods with deep texture features
obtained using a self-supervised contrastive learning approach. We identify
clusters in the representations that correspond well with classical
descriptions of hippocampal subfields, lending validity to the developed
methodology.