FastSurfer-HypVINN: Automated sub-segmentation of the hypothalamus and adjacent structures on high-resolutional brain MRI

Santiago Estrada; David Kügler; Emad Bahrami; Peng Xu; Dilshad Mousa; Monique M B Breteler; N Ahmad Aziz; Martin Reuter

doi:10.1162/imag_a_00034

FastSurfer-HypVINN: Automated sub-segmentation of the hypothalamus and adjacent structures on high-resolutional brain MRI

Imaging Neurosci (Camb). 2023 Nov 21:1:1-32. doi: 10.1162/imag_a_00034. eCollection 2023 Nov 1.

Authors

Santiago Estrada^{1

2}, David Kügler¹, Emad Bahrami^{1

3}, Peng Xu², Dilshad Mousa², Monique M B Breteler^{2

4}, N Ahmad Aziz^{2

5}, Martin Reuter^{1

6

7}

Affiliations

¹ AI in Medical Imaging, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.
² Population Health Sciences, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.
³ Computer Science Department, University of Bonn, Bonn, Germany.
⁴ Institute for Medical Biometry, Informatics and Epidemiology (IMBIE), Faculty of Medicine, University of Bonn, Bonn, Germany.
⁵ Department of Neurology, Faculty of Medicine, University of Bonn, Bonn, Germany.
⁶ A.A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, United States.
⁷ Department of Radiology, Harvard Medical School, Boston, MA, United States.

Abstract

The hypothalamus plays a crucial role in the regulation of a broad range of physiological, behavioral, 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) magnetic resonance imaging (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-an independent dataset never encountered during training with different acquisition parameters and demographics. Finally, HypVINN can perform the segmentation in less than a minute (graphical processing unit [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.

Keywords: deep learning; hetero-modal; high-resolution; hypothalamic sub-segmentation; structural MRI.