  Postmortem MRI allows brain anatomy to be examined at high resolution and to
link pathology measures with morphometric measurements. However, automated
segmentation methods for brain mapping in postmortem 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 of 135 postmortem human brain tissue specimens imaged
at 0.3 mm$^{3}$ isotropic using a T2w sequence on a 7T whole-body MRI scanner.
We developed a deep learning pipeline to segment the cortical mantle by
benchmarking the performance of nine deep neural architectures, followed by
post-hoc topological correction. We then segment four subcortical structures
(caudate, putamen, globus pallidus, and thalamus), white matter
hyperintensities, and the normal appearing white matter. We show generalizing
capabilities across whole brain hemispheres in different specimens, and also on
unseen images acquired at 0.28 mm^3 and 0.16 mm^3 isotropic T2*w FLASH sequence
at 7T. We then compute localized cortical thickness and volumetric measurements
across key regions, and link them with semi-quantitative neuropathological
ratings. Our code, Jupyter notebooks, and the containerized executables are
publicly available at: https://pulkit-khandelwal.github.io/exvivo-brain-upenn
