  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.
