Abstract #1133

Fully automated grey and white matter segmentation of the cervical cord in vivo

Ferran Prados^1,2, Manuel Jorge Cardoso¹, Marios C Yiannakas², Luke R Hoy², Elisa Tebaldi², Hugh Kearney², Martina D Liechti², David H Miller², Olga Ciccarelli², Claudia Angela Michela Gandini Wheeler-Kingshott^2,3, and Sebastien Ourselin¹

¹Translational Imaging Group, Medical Physics and Biomedical Engineering, University College London, London, United Kingdom, ²NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, University College London, London, United Kingdom, ³Brain Connectivity Center, C. Mondino National Neurological Institute, Pavia, Italy

We propose and validate a new fully automated spinal cord (SC) segmentation technique that incorporates two different multi-atlas segmentation propagation and fusion techniques: Optimized PatchMatch Label fusion (OPAL) and Similarity and Truth Estimation for Propagated Segmentations (STEPS). We collaboratively join the advantages of each method to obtain the most accurate SC segmentation. The new method reaches the inter-rater variability, providing automatic segmentations equivalents to inter-rater segmentations in terms of DSC 0.97 for whole cord for any subject.

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