Gray Matter Segmentation in Ultra High Resolution 7 Tesla ex vivo T2w MRI of Human Brain Hemispheres

TL;DR

This paper introduces a high-resolution ex vivo 7 Tesla MRI dataset of human brains, benchmarks neural network segmentation methods, and demonstrates their generalization across specimens and imaging protocols, advancing automated cortical segmentation.

Contribution

It provides a new high-resolution ex vivo brain dataset, evaluates multiple neural network architectures for cortical segmentation, and shows their robustness across different specimens and imaging conditions.

Findings

Neural networks achieved high segmentation accuracy across specimens.

Models generalized well to unseen images from different MRI protocols.

Publicly available dataset and code facilitate further research.

Abstract

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.