Ferran Prados1,2, 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-Kingshott2,3, 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.
