H-SynEx: Using synthetic images and ultra-high resolution ex vivo MRI
  for hypothalamus subregion segmentation
Livia Rodrigues Martina Bocchetta Oula Puonti Douglas Greve Ana Carolina Londe Marcondes França Simone Appenzeller Juan Eugenio Iglesias Leticia Rittner
Purpose: To develop a method for automated segmentation of hypothalamus
subregions informed by ultra-high resolution ex vivo magnetic resonance images
(MRI), which generalizes across MRI sequences and resolutions without
retraining.
  Materials and Methods: We trained our deep learning method, H-synEx, with
synthetic images derived from label maps built from ultra-high resolution ex
vivo MRI scans, which enables finer-grained manual segmentation when compared
with 1mm isometric in vivo images. We validated this retrospective study using
1535 in vivo images from six datasets and six MRI sequences. The quantitative
evaluation used the Dice Coefficient (DC) and Average Hausdorff distance (AVD).
Statistical analysis compared hypothalamic subregion volumes in controls,
Alzheimer's disease (AD), and behavioral variant frontotemporal dementia
(bvFTD) subjects using the area under the curve (AUC) and Wilcoxon rank sum
test.
  Results: H-SynEx can segment the hypothalamus across various MRI sequences,
encompassing FLAIR sequences with significant slice spacing (5mm). Using
hypothalamic volumes on T1w images to distinguish control from AD and bvFTD
patients, we observed AUC values of 0.74 and 0.79 respectively. Additionally,
AUC=0.66 was found for volume variation on FLAIR scans when comparing control
and non-patients.
  Conclusion: Our results show that H-SynEx successfully leverages information
from ultra-high resolution scans to segment in vivo from different MRI
sequences such as T1w, T2w, PD, qT1, FA, and FLAIR. We also found that our
automated segmentation was able to discriminate controls versus patients on
FLAIR images with 5mm spacing. H-SynEx is openly available at
https://github.com/liviamarodrigues/hsynex.