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Abstract #3221

Automatic brain segmentation framework for bias field rich cranial MRI scans of rats and mice via similarity invariant shape priors

Jacob Daniel Kirstejn Hansen1, Fran├žois Bernard Lauze1, Sune Darkner1, Julia M Huntenburg2, Kristian Nygaard Mortensen3, Simon Sanggaard3, Hedok Lee4, Helene Benveniste4, and Maiken Nedergaard3,5

1Department of Computer Science, University of Copenhagen, Copenhagen, Denmark, 2Champalimaud Centre for the Unknown, Champalimaud Research, Lisbon, Portugal, 3Center for Translational Neuromedicine, University of Copenhagen, Copenhagen, Denmark, 4Department of Anesthesiology, Yale School of Medicine, Yale University, New Haven, CT, United States, 5Center for Translational Neuromedicine, University of Rochester, Rochester, NY, United States

This abstract presents an extension to our previous work for the extraction of rat brain tissue and internal cerebrospinal fluid networks in MR imaging of rat crania that display severe bias fields. This work contributes automation and robustness in the skull stripping module by introducing an automatic similarity invariant shape prior segmentation method. We demonstrate the capabilities of our framework on both rat brain as well as mouse brain data, using the same minimal number of rat brain priors.

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