A deep learning toolbox for automatic segmentation of subcortical limbic structures from MRI images.

Journal: NeuroImage
PMID: 34571161

Abstract

A tool was developed to automatically segment several subcortical limbic structures (nucleus accumbens, basal forebrain, septal nuclei, hypothalamus without mammillary bodies, the mammillary bodies, and fornix) using only a T1-weighted MRI as input. This tool fills an unmet need as there are few, if any, publicly available tools to segment these clinically relevant structures. A U-Net with spatial, intensity, contrast, and noise augmentation was trained using 39 manually labeled MRI data sets. In general, the Dice scores, true positive rates, false discovery rates, and manual-automatic volume correlation were very good relative to comparable tools for other structures. A diverse data set of 698 subjects were segmented using the tool; evaluation of the resulting labelings showed that the tool failed in less than 1% of cases. Test-retest reliability of the tool was excellent. The automatically segmented volume of all structures except mammillary bodies showed effectiveness at detecting either clinical AD effects, age effects, or both. This tool will be publicly released with FreeSurfer (surfer.nmr.mgh.harvard.edu/fswiki/ScLimbic). Together with the other cortical and subcortical limbic segmentations, this tool will allow FreeSurfer to provide a comprehensive view of the limbic system in an automated way.

Authors

  • Douglas N Greve
    Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, 02129, United States; Department of Radiology, Harvard Medical School, United States.
  • Benjamin Billot
    Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, UK.
  • Devani Cordero
    Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA.
  • Andrew Hoopes
    Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, 02129, United States.
  • Malte Hoffmann
    Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA; Harvard Medical School, Radiology Department, Boston, MA, USA.
  • Adrian V Dalca
    Computer Science and Artificial Intelligence Lab, MIT, Cambridge, MA, USA; Martinos Center for Biomedical Imaging, Massachusetts General Hospital, HMS, Charlestown, MA, USA; School of Electrical and Computer Engineering, Cornell University, Ithaca, NY, USA. Electronic address: adalca@csail.mit.edu.
  • Bruce Fischl
    Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, 02129, United States; Department of Radiology, Harvard Medical School, United States; Division of Health Sciences and Technology and Engineering and Computer Science MIT, Cambridge, MA, United States.
  • Juan Eugenio Iglesias
    Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, UK; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, USA; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Boston, USA. Electronic address: e.iglesias@ucl.ac.uk.
  • Jean C Augustinack
    Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA; Harvard Medical School, Radiology Department, Boston, MA, USA.

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