Abstract
Post mortem studies have shown volume changes of the hypothalamus in psychiatric patients. With 7T magnetic resonance imaging this effect can now be investigated in vivo in detail. To benefit from the sub-millimeter resolution requires an improved segmentation procedure. The traditional anatomical landmarks of the hypothalamus were refined using 7T T1-weighted magnetic resonance images. A detailed segmentation algorithm (unilateral hypothalamus) was developed for colour-coded, histogram-matched images, and evaluated in a sample of 10 subjects. Test-retest and inter-rater reliabilities were estimated in terms of intraclass-correlation coefficients (ICC) and Dice's coefficient (DC). The computer-assisted segmentation algorithm ensured test-retest reliabilities of ICC≥.97 (DC≥96.8) and inter-rater reliabilities of ICC≥.94 (DC = 95.2). There were no significant volume differences between the segmentation runs, raters, and hemispheres. The estimated volumes of the hypothalamus lie within the range of previous histological and neuroimaging results. We present a computer-assisted algorithm for the manual segmentation of the human hypothalamus using T1-weighted 7T magnetic resonance imaging. Providing very high test-retest and inter-rater reliabilities, it outperforms former procedures established at 1.5T and 3T magnetic resonance images and thus can serve as a gold standard for future automated procedures.
Highlights
The hypothalamus is a small grey matter brain region of the diencephalon that surrounds the anterior portion of the third ventricle
We developed the first reproducible method for the volumetric measurement of the human hypothalamus with 7T MR images
Due to sub-millimeter resolution, we were able to refine the anatomical landmarks that have so far guided the segmentation of the hypothalamic region on MR images
Summary
Background The hypothalamus is a small grey matter brain region of the diencephalon that surrounds the anterior portion of the third ventricle. Since the hypothalamic region is less than 4 cm in size, and hard to distinguish from its surroundings, submillimeter magnetic resonance imaging (MRI) is essential for its in vivo structural investigation. To benefit from the improved resolution a high measurement accuracy of the volumetric technique is vitally important. To this end, (semi-) automated segmentation algorithms might seem preferable, but existing software applications are limited to segmenting tissue types or larger brain structures. In case of the hypothalamus the tissue segmentation approach is bound to fail at least superiorly and laterally, where the hypothalamic grey matter is bordered by nonhypothalamic grey structures [10]. Manual segmentation guided by anatomical landmarks is still state-of-the-art for defining the human hypothalamus on MR images
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