A simple rapid process for semi-automated brain extraction from magnetic resonance images of the whole mouse head

Abstract

Background Magnetic resonance imaging (MRI) is a well-developed technique in neuroscience. Limitations in applying MRI to rodent models of neuropsychiatric disorders include the large number of animals required to achieve statistical significance, and the paucity of automation tools for the critical early step in processing, brain extraction, which prepares brain images for alignment and voxel-wise statistics. New method This novel timesaving automation of template-based brain extraction (“skull-stripping”) is capable of quickly and reliably extracting the brain from large numbers of whole head images in a single step. The method is simple to install and requires minimal user interaction. Results This method is equally applicable to different types of MR images. Results were evaluated with Dice and Jacquard similarity indices and compared in 3D surface projections with other stripping approaches. Statistical comparisons demonstrate that individual variation of brain volumes are preserved. Comparison with existing methods A downloadable software package not otherwise available for extraction of brains from whole head images is included here. This software tool increases speed, can be used with an atlas or a template from within the dataset, and produces masks that need little further refinement. Conclusions Our new automation can be applied to any MR dataset, since the starting point is a template mask generated specifically for that dataset. The method reliably and rapidly extracts brain images from whole head images, rendering them useable for subsequent analytical processing. This software tool will accelerate the exploitation of mouse models for the investigation of human brain disorders by MRI

Automated Computational Processing of 3‐D MR Images of Mouse Brain for Phenotyping of Living Animals

“…CUDA speeds up image processing and can be downloaded from https://developer.nvidia.com/cuda-downloads . NiftyReg is necessary for our in-house SkullStrip programs (Delora et al, 2016), and as an option for alignments later in this protocol. NiftyReg is available for free download at http://cmictig.cs.ucl.ac.uk/wiki/index.php/NiftyReg_install.…”

“…modal_scale_nii.m is a MATLAB routine developed in the Jacobs’ lab, and has been applied in a variety of papers (Bearer et al, 2007a; Bearer et al, 2007b; Delora et al, 2016; Gallagher et al, 2013; Gallagher et al, 2012; Malkova et al, 2014; Zhang et al, 2010a) although the actual processing code is previously unpublished. The “modal_scale” code is provided here with this protocol for the first time (see Supplemental Materials).…”

“…skullstripper.py is a program we created (Delora et al, 2016). It can be downloaded from our publication (Delora et al, 2016) or from our STMC website (http://stmc.health.unm.edu/tools-and-data/index.html).…”

“…It can be downloaded from our publication (Delora et al, 2016) or from our STMC website (http://stmc.health.unm.edu/tools-and-data/index.html). The computer code, installation instructions, and application methods are found in the Supplemental Materials for that paper.…”

“…After visual inspection, we want to ensure that all images in the dataset have the same dimensions (number of voxels in each dimension), voxel size (in mm), file type (char, short, int, float, double, etc. ), byte order (Little or Big Endian), signed or unsigned, and bit type (typically either 8, 16, or 32) (Delora et al, 2016; Woods et al, 1998). Settings for each of these parameters can differ between images, and prior to further processing they must be made uniform across all images in a particular dataset.…”