  The hypothalamus is a small structure located in the center of the brain and
is involved in significant functions such as sleeping, temperature, and
appetite control. Various neurological disorders are also associated with
hypothalamic abnormalities. Automated image analysis of this structure from
brain MRI is thus highly desirable to study the hypothalamus in vivo. However,
most automated segmentation tools currently available focus exclusively on T1w
images. In this study, we introduce H-SynEx, a machine learning method for
automated segmentation of hypothalamic subregions that generalizes across
different MRI sequences and resolutions without retraining. H-synEx was trained
with synthetic images built from label maps derived from ultra-high resolution
ex vivo MRI scans, which enables finer-grained manual segmentation when
compared with 1mm isometric in vivo images. We validated our method using Dice
Coefficient (DSC) and Average Hausdorff distance (AVD) across in vivo images
from six different datasets with six different MRI sequences (T1, T2, proton
density, quantitative T1, fractional anisotrophy, and FLAIR). Statistical
analysis compared hypothalamic subregion volumes in controls, Alzheimer's
disease (AD), and behavioral variant frontotemporal dementia (bvFTD) subjects
using the Area Under the Receiving Operating Characteristic curve (AUROC) and
Wilcoxon rank sum test. Our results show that H-SynEx successfully leverages
information from ultra-high resolution scans to segment in vivo from different
MRI sequences. Our automated segmentation was able to discriminate controls
versus Alzheimer's Disease patients on FLAIR images with 5mm spacing. H-SynEx
is openly available at https://github.com/liviamarodrigues/hsynex.
