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
this https URL

开发了一种基于超高分辨率离体磁共振成像（MRI）的下丘脑亚区自动分割方法，可跨 MRI 序列和分辨率进行普适化，无需重新训练。

H-SynEx：使用合成图像和超高分辨率体外 MRI 进行下丘脑亚区分割

H-SynEx: Using synthetic images and ultra-high resolution ex vivo MRI  for hypothalamus subregion segmentation

The hypothalamus plays a crucial role in the regulation of a broad range of
physiological, behavioural, and cognitive functions. However, despite its
importance, only a few small-scale neuroimaging studies have investigated its
substructures, likely due to the lack of fully automated segmentation tools to
address scalability and reproducibility issues of manual segmentation. While
the only previous attempt to automatically sub-segment the hypothalamus with a
neural network showed promise for 1.0 mm isotropic T1-weighted (T1w) MRI, there
is a need for an automated tool to sub-segment also high-resolutional (HiRes)
MR scans, as they are becoming widely available, and include structural detail
also from multi-modal MRI. We, therefore, introduce a novel, fast, and fully
automated deep learning method named HypVINN for sub-segmentation of the
hypothalamus and adjacent structures on 0.8 mm isotropic T1w and T2w brain MR
images that is robust to missing modalities. We extensively validate our model
with respect to segmentation accuracy, generalizability, in-session test-retest
reliability, and sensitivity to replicate hypothalamic volume effects (e.g.
sex-differences). The proposed method exhibits high segmentation performance
both for standalone T1w images as well as for T1w/T2w image pairs. Even with
the additional capability to accept flexible inputs, our model matches or
exceeds the performance of state-of-the-art methods with fixed inputs. We,
further, demonstrate the generalizability of our method in experiments with 1.0
mm MR scans from both the Rhineland Study and the UK Biobank. Finally, HypVINN
can perform the segmentation in less than a minute (GPU) and will be available
in the open source FastSurfer neuroimaging software suite, offering a
validated, efficient, and scalable solution for evaluating imaging-derived
phenotypes of the hypothalamus.

我们提出了一种名为 HypVINN 的新型、快速、全自动的深度学习方法，用于对 0.8 毫米等效的 T1w 和 T2w 脑 MR 图像进行下丘脑及邻近结构的子分割，具有较高的分割性能和广泛的适应性。