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Abstract #3401

Robust and fully-automated atrophy measure for multiple sclerosis disease

Ferran Prados 1,2 , Manuel Jorge Cardoso 1 , David M Cash 1,3 , Marc Modat 1,3 , Claudia A. M. Wheeler-Kingshott 2 , and Sebastien Ourselin 1,3

1 Centre for Medical Image Computing, Department Medical Physics and Bioengineering, University College of London, London, United Kingdom, 2 NMR Research Unit, Department of Neuroinflammation, Queen Square MS Centre, UCL Institute of Neurology, London, United Kingdom, 3 Dementia Research Centre, Department of Neurodegenerative Disease, UCL Institute of Neurology, London, United Kingdom

Brain atrophy is an accurate predictor of multiple sclerosis (MS) pathology. In this work, we present a generalised formulation of the Boundary Shift Integral (GBSI) using probabilistic segmentations. This method adaptively estimates a non-binary XOR region-of-interest from probabilistic brain segmentations of the baseline and repeat scans, in order to better localise and capture the brain atrophy. We evaluate the proposed method by comparing the sample size requirements for an hypothetical clinical trial of MS disease to that needed for SIENA. GBSI results reduced sample size, providing increased sensitivity to disease changes through the use of the probabilistic XOR region.

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