A Deep Learning Toolbox for Automatic Segmentation of Subcortical Limbic Structures from MRI Images

Overview

Journal Neuroimage

Specialty Radiology

Date 2021 Sep 27

PMID 34571161

Citations 21

Authors

Douglas N Greve

Benjamin Billot

Devani Cordero

Andrew Hoopes

Malte Hoffmann

Adrian V Dalca

Bruce Fischl

Juan Eugenio Iglesias

Jean C Augustinack

Affiliations

Soon will be listed here.

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.

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