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

Douglas N Greve; Benjamin Billot; Devani Cordero; Andrew Hoopes; Malte Hoffmann; Adrian V Dalca; Bruce Fischl; Juan Eugenio Iglesias; Jean C Augustinack

doi:10.1016/j.neuroimage.2021.118610

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

Neuroimage. 2021 Dec 1:244:118610. doi: 10.1016/j.neuroimage.2021.118610. Epub 2021 Sep 25.

Authors

Douglas N Greve¹, Benjamin Billot², Devani Cordero³, Andrew Hoopes³, Malte Hoffmann⁴, Adrian V Dalca⁵, Bruce Fischl⁵, Juan Eugenio Iglesias⁶, Jean C Augustinack⁴

Affiliations

¹ Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA; Harvard Medical School, Radiology Department, Boston, MA, USA. Electronic address: dgreve@mgh.harvard.edu.
² Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, UK.
³ Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA.
⁴ Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA; Harvard Medical School, Radiology Department, Boston, MA, USA.
⁵ Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA; Harvard Medical School, Radiology Department, Boston, MA, USA; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Boston, USA.
⁶ Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA; Harvard Medical School, Radiology Department, Boston, MA, USA; Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, UK; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Boston, USA.

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.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

Adolescent
Adult
Aged
Aged, 80 and over
Basal Forebrain / diagnostic imaging
Deep Learning*
Female
Fornix, Brain / diagnostic imaging
Humans
Limbic System / diagnostic imaging*
Magnetic Resonance Imaging / methods*
Male
Middle Aged
Nucleus Accumbens / diagnostic imaging
Reproducibility of Results
Septal Nuclei / diagnostic imaging
Young Adult

Abstract

Publication types

MeSH terms

Grants and funding