title: Automated segmentation of lateral ventricles from human and primate magnetic resonance images using cognition network technology.
abstract: Automatic segmentation of different types of tissue from magnetic resonance images is of great importance for clinical and research applications, particularly large-scale and longitudinal studies of brain pathology. We developed a fully automated algorithm for the segmentation of lateral ventricles from cranial magnetic resonance images. This problem is of interest in the study of schizophrenia, dementia and other neuropsychiatric disorders. Our algorithm achieves comparable results to expert human raters. The theoretical approach, which is based on an emerging object-oriented technology that has been adapted and evaluated to process 3D data for the first time, may, in the future, be transferred to other important problems of magnetic resonance image analysis like gray/white matter segmentation.
authors.affiliation: Brain Imaging Center, Johann Wolfgang Goethe University Frankfurt am Main, Schleusenweg 2-16, 60528 Frankfurt am Main, Germany. schoenmeyer@bic.uni-frankfurt.de
authors.forename: Ralf
authors.initials: R
authors.lastname: Schönmeyer
authors.forename: David
authors.initials: D
authors.lastname: Prvulovic
authors.forename: Anna
authors.initials: A
authors.lastname: Rotarska-Jagiela
authors.forename: Corinna
authors.initials: C
authors.lastname: Haenschel
authors.forename: David E J
authors.initials: DE
authors.lastname: Linden
mesh_terms: D000465:Algorithms; D000818:Animals; D002552:Cerebral Ventricles / Q000033:anatomy & histology*; D006801:Humans; D007091:Image Processing, Computer-Assisted; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008251:Macaca; D008279:Magnetic Resonance Imaging; D015588:Observer Variation