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Abstract #5293

A fully unsupervised method for spinal cord lesion segmentation in Multiple Sclerosis

Carole Hélène Sudre1,2, Ferran Prados1,3, Rosanna Cortese3, Marios Yiannakas3, Hugh Kearney3, Olga Ciccarelli3, Sébastien Ourselin1,2, Claudia Angela Gandini Wheeler-Kingshott3,4,5, and M. Jorge Cardoso1,2

1Translational Imaging Group, CMIC, Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom, 2UCL Institute of Neurology, Dementia Research Centre, University College London, London, United Kingdom, 3Queen Square MS Centre, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom, 4Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy, 5Brain MRI 3T Research Centre, C. Mondino National Neurological Institute, Pavia, Italy

The presence of focal lesions in the spinal cord is an important diagnostic criteria for Multiple Sclerosis (MS). Accurate estimation of lesion volume is important for monitoring disease progression over time. However, manual and automated lesion segmentation for volume estimation remain challenging, since they rely respectively on the skills of the rater or on the automated criteria set within the algorithms. In this work, we present an adaptation to the spinal cord, of a fully unsupervised hierarchical model selection framework that automatically detects abnormality tissue patterns without any a priori knowledge on pathology location.

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