  Ex vivo MRI of the brain provides remarkable advantages over in vivo MRI for
visualizing and characterizing detailed neuroanatomy. However, automated
cortical segmentation methods in ex vivo MRI are not well developed, primarily
due to limited availability of labeled datasets, and heterogeneity in scanner
hardware and acquisition protocols. In this work, we present a high resolution
7 Tesla dataset of 32 ex vivo human brain specimens. We benchmark the cortical
mantle segmentation performance of nine neural network architectures, trained
and evaluated using manually-segmented 3D patches sampled from specific
cortical regions, and show excellent generalizing capabilities across whole
brain hemispheres in different specimens, and also on unseen images acquired at
different magnetic field strength and imaging sequences. Finally, we provide
cortical thickness measurements across key regions in 3D ex vivo human brain
images. Our code and processed datasets are publicly available at
https://github.com/Pulkit-Khandelwal/picsl-ex-vivo-segmentation.
