Abstract
Segmentation of magnetic resonance imaging (MRI) data is required for many applications, such as the comparison of different structures or time points, and for annotation purposes. Currently, the gold standard for automated image segmentation is nonlinear atlas-based segmentation. However, these methods are either not sufficient or highly time consuming for mouse brains, owing to the low signal to noise ratio and low contrast between structures compared with other applications. We present a novel generic approach to reduce processing time for segmentation of various structures of mouse brains, in vivo and ex vivo. The segmentation consists of a rough affine registration to a template followed by a clustering approach to refine the rough segmentation near the edges. Compared with manual segmentations, the presented segmentation method has an average kappa index of 0.7 for 7 of 12 structures in in vivo MRI and 11 of 12 structures in ex vivo MRI. Furthermore, we found that these results were equal to the performance of a nonlinear segmentation method, but with the advantage of being 8 times faster. The presented automatic segmentation method is quick and intuitive and can be used for image registration, volume quantification of structures, and annotation.
Highlights
Segmentation of magnetic resonance imaging (MRI) data is required for many applications, such as the comparison of different structures or time points, and for annotation purposes
We found that manually segmenting the MRI volume as a preprocessing step was sufficient to register single two-dimensional hematoxylin and eosin
The aim of this research was to develop a fully automated segmentation method for in vivo and ex vivo mouse brains, which is fast and accurate enough to be used as an intermediate step for registration, as well as generic enough to be used on different small-animal MRIs
Summary
Segmentation of magnetic resonance imaging (MRI) data is required for many applications, such as the comparison of different structures or time points, and for annotation purposes. # 2009 BC Decker Inc stained histologic sections to an ex vivo MRI volume of the mouse brain.[8] The aim of this research was to develop a fully automated segmentation method for in vivo and ex vivo mouse brains, which is fast and accurate enough to be used as an intermediate step for registration, as well as generic enough to be used on different small-animal MRIs. Automated segmentation of mouse brain MRIs is still very challenging, in contrast to the automated segmentation of human brain MRIs.[9] most algorithms developed for the human brain segmentation are not directly applicable to mouse brain images, as Tohka and colleagues recently presented.[10] the requirements for the small-animal imaging with MRI are similar, performing volumetric measurement or shape analysis is useful for the annotation purposes or quantitative phenotyping of (transgenic) mouse models. These segmentation problems in mice brain MRI are mostly due to artifacts caused by the MRI scanner, deformations caused by the excision of the brain, and, most importantly, less contrast between brain structures and a lower signal to noise ratio compared with human MRI
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