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Fast segmentation with the NextBrain histological atlas
Preprint   Open access

Fast segmentation with the NextBrain histological atlas

Oula Puonti, Jackson Nolan, Robert Dicamillo, Yael Balbastre, Adria Casamitjana, Matteo Mancini, Eleanor Robinson, Loic Peter, Roberto Annunziata, Juri Althonayan, …
bioRxiv
Cold Spring Harbor Laboratory
09/25/2025
DOI: 10.1101/2025.09.22.673638
PMCID: PMC12485876
PMID: 41040372
url
https://doi.org/10.1101/2025.09.22.673638View
Preprint (Author's original)This preprint has not been evaluated by subject experts through peer review. Preprints may undergo extensive changes and/or become peer-reviewed journal articles. Open Access

Abstract

Structural brain analysis at the subregion level offers critical insights into healthy aging and neurodegenerative diseases. The NextBrain histological atlas was recently introduced to support such fine-grained investigations, but its existing Bayesian segmentation framework remains computationally prohibitive, particularly for large-scale studies. We present a new, open-source tool that dramatically accelerates segmentation using a hybrid approach combining: machine learning, contrast-adaptive segmentation; target-specific image synthesis; and fast diffeomorphic registration (all three with GPU support). Our method enables highly granular segmentation of brain MRI scans of any resolution and contrast ( in vivo or ex vivo ) at a fraction of the computational cost of the original method ( < 5 minutes on a GPU). We validate our tool on four different modalities ( in vivo MRI, ex vivo MRI, HiP-CT, and photography) across a total of approximately 4,000 brain scans. Our results demonstrate that the accelerated approach achieves comparable accuracy to the original method in terms of Dice scores, while reducing runtime by over an order of magnitude. This work enables high-resolution anatomical analysis at unprecedented scale and flexibility, providing a practical solution for large neuroimaging studies. Our tool is publicly available in FreeSurfer ( https://surfer.nmr.mgh.harvard.edu/fswiki/HistoAtlasSegmentation ).Structural brain analysis at the subregion level offers critical insights into healthy aging and neurodegenerative diseases. The NextBrain histological atlas was recently introduced to support such fine-grained investigations, but its existing Bayesian segmentation framework remains computationally prohibitive, particularly for large-scale studies. We present a new, open-source tool that dramatically accelerates segmentation using a hybrid approach combining: machine learning, contrast-adaptive segmentation; target-specific image synthesis; and fast diffeomorphic registration (all three with GPU support). Our method enables highly granular segmentation of brain MRI scans of any resolution and contrast ( in vivo or ex vivo ) at a fraction of the computational cost of the original method ( < 5 minutes on a GPU). We validate our tool on four different modalities ( in vivo MRI, ex vivo MRI, HiP-CT, and photography) across a total of approximately 4,000 brain scans. Our results demonstrate that the accelerated approach achieves comparable accuracy to the original method in terms of Dice scores, while reducing runtime by over an order of magnitude. This work enables high-resolution anatomical analysis at unprecedented scale and flexibility, providing a practical solution for large neuroimaging studies. Our tool is publicly available in FreeSurfer ( https://surfer.nmr.mgh.harvard.edu/fswiki/HistoAtlasSegmentation ).

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