Automated Segmentation of in Vivo and Ex Vivo Mouse Brain Magnetic Resonance Images

Automated Segmentation of in Vivo and Ex Vivo Mouse Brain Magnetic Resonance Images

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Segmentation of magnetic resonance imaging (MRI) data is required for many applications, such as the comparison of different structures The Types and Pattern of Use of Mobile Health Applications Among the General Population: A Cross-Sectional Study from Selangor, Malaysia 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 First study on microscopic and molecular detection of Acanthocheilonema reconditum and Leishmania infantum coinfection in dogs in Southwest Colombia 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.