Automated segmentation of lateral ventricles from human and primate magnetic resonance images using cognition network technology

Schönmeyer, Ralf, Prvulovic, David, Rotarska-Jagiela, Anna, Haenschel, Corinna and Linden, David E. J. ORCID: https://orcid.org/0000-0002-5638-9292 2006. Automated segmentation of lateral ventricles from human and primate magnetic resonance images using cognition network technology. Magnetic Resonance Imaging 24 (10) , pp. 1377-1387. 10.1016/j.mri.2006.08.013

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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.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Psychology Medicine MRC Centre for Neuropsychiatric Genetics and Genomics (CNGG) Neuroscience and Mental Health Research Institute (NMHRI)
Uncontrolled Keywords:	Automatic segmentation; Brain imaging; Lateral ventricles; Object-oriented image analysis; Nonhuman primates
Publisher:	Elsevier
ISSN:	0730-725X
Date of Acceptance:	28 August 2006
Last Modified:	20 Oct 2022 09:42
URI:	https://orca.cardiff.ac.uk/id/eprint/32879

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