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, François Bernard Lauze¹, Sune Darkner¹, Julia M Huntenburg², Kristian Nygaard Mortensen³, Simon Sanggaard³, Hedok Lee⁴, Helene Benveniste⁴, and Maiken Nedergaard^3,5

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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