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We show that a suite of segmentation tools (Avants, Epstein et al., 2008) designed for human neuroimaging can be incorporated into a pipeline for segmenting mouse brain images acquired with multispectral magnetic resonance imaging (MR) protocols.
Yet challenges remain when applying SVM on segmenting mouse brain images.
The input dataset of mouse brain images were contributed by the Allen Institute for Brain Science and other research organizations using a standardized protocol.
In particular, for mouse brain images obtained with a surface RF coil, no arteries are typically visible from which to measure an AIF.
An 11.7 T 89 mm vertical bore Bruker BioSpin Avance DRX500 scanner (Bruker BioSpin Inc, Billerica, MA) equipped with a Micro2.5 gradient system was used to acquire all mouse brain images and spectroscopic data with a 35 mm linear birdcage radio frequency (RF) coil.
We thank Bernhard Herrmann for giving access to the in situ mouse brain images shown in Figure 3.
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The total size of the reconstructed mouse brain image is (sim 40.9 TB.
A mouse brain imaged at light-microscopic resolution is about a terabyte of data, and 1 mm3 of the brain at EM resolution is about half a petabyte.
This is due to the fact that each high-resolution mouse brain image is a very large data set and it is a multiclass classification problem with extremely imbalanced data size for different classes.
This method had been successfully applied to reconstructing neuronal connections in the most detailed mouse brain image atlas to date (Li et al., 2010).
Motivation: Recently, confocal light sheet microscopy has enabled high-throughput acquisition of whole mouse brain 3D images at the micron scale resolution.
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