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Abstract #1449

A probabilistic framework to learn average shaped tissue templates and its application to spinal cord image segmentation

Claudia Blaiotta1, Patrick Freund1,2, Armin Curt2, Jorge Cardoso3, and John Ashburner1

1Wellcome Trust Centre for Neuroimaging, University College London, London, United Kingdom, 2Spinal Cord Injury Center Balgrist, University of Zurich, Zurich, Switzerland, 3Centre for Medical Image Computing, University College London, London, United Kingdom

Magnetic resonance imaging of the spinal cord has a pre-eminent role for understanding the physiopathology of neurological disorders; nevertheless it is confronted with numerous technical challenges, which currently limit its applicability. In this work we focus on the problem of automatically extracting and segmenting the cord, a crucial processing step for neuroimaging studies. We present a novel computational framework that allows delineating structures within the cord, thus providing a reliable and fast alternative to manual segmentation. We test the method on a data set of high-resolution cervical scans and demonstrate the consistency of our results with expert manual annotation.

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