Fast segmentation with the NextBrain histological atlas.
Authors
Affiliations (15)
Affiliations (15)
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital - Amager and Hvidovre, Copenhagen, Denmark.
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States.
- Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom.
- Department of Experimental Psychology, University College London, London, United Kingdom.
- Research Institute of Computer Vision and Robotics, University of Girona, Girona, Spain.
- Enrico Fermi Research Center, Rome, Italy.
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom.
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, United States.
- Advanced Research Computing Centre, University College London, London, United Kingdom.
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States.
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom.
- Neuroradiological Academic Unit, Department of Translational Neuroscience and Stroke, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom.
- Department of Psychological and Brain Sciences, University of Iowa, Iowa City, IA, United States.
- Centre for Cognitive and Clinical Neuroscience, Division of Psychology, Department of Life Sciences, College of Health, Medicine and Life Sciences, Brunel University of London, London, United Kingdom.
- Queen Square Brain Bank for Neurological Disorders, Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom.
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 (<i>in vivo or ex vivo</i>) at a fraction of the computational cost of the original method ( <math xmlns="http://www.w3.org/1998/Math/MathML"><mo><</mo></math> 5 minutes on a GPU). We validate our tool on four different modalities (<i>in vivo</i> MRI, <i>ex vivo</i> 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).