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Abstract #0952

Fully Automatic Segmentation of the Amygdala on High Resolution T1 Images Using a Shape Model

Frank Thiele1, 2, Lukas Scheef2, Fabian Wenzel3, Carsten Meyer3, Henning Boecker2, Michael Wagner4, Hans H. Schild2, Frank Jessen4

1Philips Research, Aachen, Germany; 2Radiology, University of Bonn, Bonn, Germany; 3Philips Research, Hamburg, Germany; 4Psychiatry, University of Bonn, Bonn, Germany

Automatic segmentation of amygdala volumes would present an important tool for neuroscience studies in cognition and psychiatry, and a potential diagnostic marker. In this work, a shape model is applied to T1-weighted MRI for fully automatic segmentation of the amygdala in 70 elderly normals. Segmentation is compared to manual tracing as well as a state-of-the-art atlas-based approach. The shape model is found to be a promising approach for reproducible and observer-independent analysis of the amygdala.