Cortical reconstruction and anatomical parcellation of high-resolution multi-modal postmortem ex vivo MRI of the human infant brain

Abstract

High-resolutionpostmortemmagneticresonanceimagingen- ables detailed examination of brain anatomy at spatial scales not achiev- able in vivo and provides a unique opportunity to link morphomet- ric measurements with the underlying pathology. Despite these advan- tages, robust computational tools for automated anatomical segmen- tation and cortical surface reconstruction remain limited, particularly in postmortem infant brains. Incomplete myelination, thinner cortical ribbons, small-scale neuroanatomy, evolving tissue contrast, fixation- induced signal alterations, and variability in postmortem preparation make standard neuroimaging pipelines unsuitable for postmortem in- fant MRI. In this work, we introduce a unique high-resolution multi- sequence postmortem infant MRI dataset and a unified computational framework that combines deep learning-based volumetric segmentation with surface-based cortical reconstruction and anatomical parcellation in native subject-space resolution. The framework is designed to general- ize across diverse postmortem MRI acquisition protocols, spatial resolu- tions, tissue preparation conditions, and specimen characteristics while remaining robust to substantial variability in image contrast, tissue de- formation, fixation-induced intensity changes, background signal char- acteristics, and anatomical variability encountered in postmortem in- fant MRI. We benchmark our framework against widely used contrast- agnostic and foundational brain segmentation models, demonstrating improved anatomical consistency and segmentation performance across heterogeneous high-resolution postmortem infant datasets. Our method enables morphometric analysis in native postmortem space, providing the same downstream quantitative analyses routinely available for in vivo developmental neuroimaging. The complete framework is released as open-source software with command-line workflows, containers, and comprehensive documentation to facilitate reproducible postmortem in- fant MRI analysis as part of the purple-mri package: https://purple-mri.readthedocs.io