Abstract
High-resolution postmortem 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 advantages, 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, evolving tissue contrast, fixation-induced signal alterations, and variability in postmortem preparation make standard neuroimaging pipelines unsuitable for postmortem infant 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 generalize across diverse postmortem MRI acquisition protocols, spatial resolutions, tissue preparation conditions, and specimen characteristics while remaining robust to substantial variability in image contrast, tissue deformation, fixation-induced intensity changes, background signal characteristics, and anatomical variability encountered in postmortem infant 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 infant MRI analysis as part of the package.
Links to Paper and Supplementary Materials
Main Paper (Open Access Version): https://papers.miccai.org/miccai-2026-sat/paper/PIPPI_024.pdf
SharedIt Link: Not yet available
SpringerLink (DOI): Not yet available
Supplementary Material: Not Submitted
Link to Open Review
BibTex
@InProceedings{KhaPul_Highresolution_MICCAISAT2026,
author = { Khandelwal, Pulkit AND Young, Sala AND Ngo, Nathan Xi AND Yushkevich, Paul A. AND van der Kouwe, Andre AND Haynes, Robin L. AND Kinney, Hannah C. AND Zöllei, Lilla},
title = { { High-resolution postmortem MRI of the human infant brain: cortical reconstruction and anatomical parcellation } },
booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2026 Workshops and Challenges},
year = {2026},
publisher = {Springer Nature Switzerland},
volume = {LNCS 17257},
month = {pending},
page = {pending}
}