It's too late for Lenin, but the University of Pennsylvania's high-resolution MRI scan of a dead person's brain is full of detail.
Ex vivo MRI of the brain provides remarkable advantages over in vivo MRI for visualizing and characterizing detailed neuroanatomy, and helps to link microscale histology studies with morphometric measurements. However, automated segmentation methods for brain mapping 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 of 135 ex vivo post-mortem human brain tissue specimens scanned 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. We then segment four subcortical structures (caudate, putamen, globus pallidus, and thalamus), white matter hyperintensities, and the normal appearing white matter. We show excellent generalizing capabilities across whole brain hemispheres in different specimens, and also on unseen images acquired at different magnetic field strengths and different imaging sequence. We then compute volumetric and localized cortical thickness measurements across key regions, a link them with semi-quantitative neuropathological ratings. Our code, containerized executables, and the processed datasets are publicly available.
I like that the YouTube video is titled "teaser," like it's a cuisine channel for zombies. There are lots of delicious images in the paper, Automated deep learning segmentation of high-resolution 7 T postmortem MRI for quantitative analysis of structure-pathology correlations in neurodegenerative diseases, which may be read on Arxiv. My own half-dead brain initally processed the first phrase in the one below as "meat thickness."