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Abstract #4704

Automatic Segmentation of Deep Grey Matter Structures for the Assessment of DTI Images

Emil Malucelli1, David Neil Manners1, Claudia Testa1, Caterina Tonon1, Giovanni Rizzo1, Valerio Carelli2, Giuseppe Nicoletti3, Aldo Quattrone3, Bruno Barbiroli1, Raffaele Lodi1

1Dipartimento di Medicina Interna dellInvecchiamento e delle Malattie Nefrologiche, University of Bologna, Bologna, Italy; 2Dipartimento di Scienze Neurologiche , University of Bologna, Bologna, Italy; 3Institute of Neurological Sciences, National Research Council, Cosenza, Italy

Time-consuming manual selection of regions of interest is currently one limiting factor in the clinical use of DTI. Methods exist to automatically identify deep brain structures using high resolution images. We evaluated the possibility of automatically segmenting structures of deep grey matter in diffusion tensor maps using high resolution T1-weighted images. We compared manual and automatic segmentation in ten controls. Results were indistinguishable for quantification of mean diffusivity in thalamus, pallidus and putamen, but not caudate. Using automatic segmentation, three patients with well-characterized neurological disorders showed differences compared to controls, in line with those expected from the known pathologies.