A template‐based automatic skull‐stripping approach for mouse brain MR microscopy

Small animal magnetic resonance microscopy (MRM) has been widely used today in computational neuroanatomy. Accurate identification of brain tissue in a mouse MRM is a critical fundamental step in neuroimaging processing, which is given less attention. This study presents an automatic skull‐stripping technique based on template models and histogram analysis. Results were evaluated by calculating the Jaccard similarity index (JSI) and boundary concordance ratio (BCR) between the automatically segmented and manually traced brain volumes. Results demonstrate that this technique accurately extracts the brain volume (mean JSI = 97.1%, BC = 94.4%). The brain extraction method presented in this study will greatly facilitate analysis of neuroimaging studies of rodent animals in neurodegenerative diseases. Microsc. Res. Tech. 2013. © 2012 Wiley Periodicals, Inc.

[1]  Alexandra Badea,et al.  Small animal imaging with magnetic resonance microscopy. , 2008, ILAR journal.

[2]  Karl J. Friston,et al.  Voxel-Based Morphometry—The Methods , 2000, NeuroImage.

[3]  Yong-Sheng Chen,et al.  Accurate and robust extraction of brain regions using a deformable model based on radial basis functions , 2009, Journal of Neuroscience Methods.

[4]  R. Mark Henkelman,et al.  High resolution three-dimensional brain atlas using an average magnetic resonance image of 40 adult C57Bl/6J mice , 2008, NeuroImage.

[5]  Veronique D. Bohbot,et al.  Maze training in mice induces MRI-detectable brain shape changes specific to the type of learning , 2011, NeuroImage.

[6]  Yutong Liu,et al.  A semi-automatic image segmentation method for extraction of brain volume from in vivo mouse head magnetic resonance imaging using Constraint Level Sets , 2009, Journal of Neuroscience Methods.

[7]  Stephen M Smith,et al.  Fast robust automated brain extraction , 2002, Human brain mapping.

[8]  J. Ashburner,et al.  Nonlinear spatial normalization using basis functions , 1999, Human brain mapping.

[9]  Richard M. Leahy,et al.  BrainSuite: An Automated Cortical Surface Identification Tool , 2000, MICCAI.

[10]  Yong Cheng,et al.  Structural MRI detects progressive regional brain atrophy and neuroprotective effects in N171-82Q Huntington's disease mouse model , 2011, NeuroImage.

[11]  N. Otsu A threshold selection method from gray level histograms , 1979 .

[12]  John Ashburner,et al.  Computational anatomy with the SPM software. , 2009, Magnetic resonance imaging.

[13]  Anqi Qiu,et al.  Robust Automatic Rodent Brain Extraction Using 3-D Pulse-Coupled Neural Networks (PCNN) , 2011, IEEE Transactions on Image Processing.

[14]  Lan Lin,et al.  MRI Template and Atlas Toolbox for the C57BL/6J Mouse Brain , 2005, Conference Proceedings. 2nd International IEEE EMBS Conference on Neural Engineering, 2005..

[15]  Seung-Schik Yoo,et al.  Interactive 3-dimensional segmentation of MRI data in personal computer environment , 2001, Journal of Neuroscience Methods.

[16]  A. J. Morton,et al.  Use of magnetic resonance imaging for anatomical phenotyping of the R6/2 mouse model of Huntington's disease , 2009, Neurobiology of Disease.

[17]  Lan Lin,et al.  Construction of mouse brain MRI templates using SPM 99 , 2003 .

[18]  J. Berger-Sweeney,et al.  Longitudinal brain MRI study in a mouse model of Rett Syndrome and the effects of choline , 2008, Neurobiology of Disease.

[19]  Alan C. Evans,et al.  A nonparametric method for automatic correction of intensity nonuniformity in MRI data , 1998, IEEE Transactions on Medical Imaging.

[20]  Christopher J. Taylor,et al.  A cooperative framework for segmentation of MRI brain scans , 2000, Artif. Intell. Medicine.