A semi-automated algorithm for hypothalamus volumetry in 3 Tesla magnetic resonance images - Wolff, Julia; Schindler, Stephanie; Lucas, Christian; Binninger, Anne-Sophie; Weinrich, Luise; Schreiber, Jan; Hegerl, Ulrich; Möller, Harald E.; Leitzke, Marco; Geyer, Stefan; Schönknecht, Peter (Psychiatry research. Neuroimaging, 2018)
•This semi-automated algorithm for hypothalamus volumetry in is fast and reliable.•Tissue probability maps are combined with T1-weighted images.•Key principles are the use of triplanar views and seed growing technique. The hypothalamus, a small diencephalic gray matter structure, is part of the limbic system. Volumetric changes of this structure occur in psychiatric diseases, therefore there is increasing interest in precise volumetry. Based on our detailed volumetry algorithm for 7 Tesla magnetic resonance imaging (MRI), we developed a method for 3 Tesla MRI, adopting anatomical landmarks and work in triplanar view. We overlaid T1-weighted MR images with gray matter-tissue probability maps to combine anatomical information with tissue class segmentation. Then, we outlined regions of interest (ROIs) that covered potential hypothalamus voxels. Within these ROIs, seed growing technique helped define the hypothalamic volume using gray matter probabilities from the tissue probability maps. This yielded a semi-automated method with short processing times of 20–40 min per hypothalamus. In the MRIs of ten subjects, reliabilities were determined as intraclass correlations (ICC) and volume overlaps in percent. Three raters achieved very good intra-rater reliabilities (ICC 0.82–0.97) and good inter-rater reliabilities (ICC 0.78 and 0.82). Overlaps of intra- and inter-rater runs were very good (≥ 89.7%). We present a fast, semi-automated method for in vivo hypothalamus volumetry in 3 Tesla MRI.