Khandelwal, P.; Young, S.; Xi Ngo, N.; Yushkevich, P. A.; van der Kouwe, A.; Haynes, R. L.; Kinney, H. C.; Zollei, L.

High-resolution postmortem (ex vivo) magnetic resonance imaging enables detailed examination of brain anatomy at spatial scales not achievable in vivo and provides a unique opportunity to link morphometric measurements with the underlying pathology. Despite these ad- vantages, robust computational tools for automated anatomical segmentation and cortical surface reconstruction remain limited, particularly in postmortem infant brains. Incomplete myelination, thinner cortical ribbons, small-scale neuroanatomy, as well as an evolving tissue contrast combined with fixation-induced signal alterations and variability in postmortem preparation make standard neuroimaging pipelines unusable for postmortem infant MRI. In this work, we introduce a one-of-its- kind multi-modal high-resolution 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. To address the pronounced domain shift inherent to postmortem MRI, we develop a postmortem-specific synthetic data generation engine (PostSynth) that explicitly models fixation-driven postmortem imaging characteristics. In particular, we incorporate postmortem-specific altered gray-white matter contrast, laminar cortical intensity heterogeneity, specimen-specific bias fields, and background signal characteristics associated with immersion media: phenomena not typically observed in in vivo data or captured by generic contrast-agnostic synthesis methods. We benchmark our frame- work against a set of widely used contrast-agnostic and foundational brain segmentation models, demonstrating improved anatomical consistency and segmentation performance in high-resolution postmortem infant data. The code is publicly available as part of the purple-mri package on GitHub: https://github.com/Pulkit-Khandelwal/purple-mri