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Brain, Head, Neck, and Eye

Hiroshi Fukuda, Yasuyuki Taki, Kazunori Sato, Kai Wu, Yoshitaka Masutani, Takeshi Hara, Chisako Muramatsu and Akinobu Shimizu

Computational Neuroanatomy

Computational neuroanatomy is a discipline focused on analyzing and modeling the anatomy of individual brains and the structural variability across a population. The goal is not only to model normal brain structures and their variations within a population but also to identify the morphological differences between normal and pathological populations. The ultimate goal is to create a human brain structure model and classify the abnormalities of the brain from structural differences. Applications are quite important in neuroscience to minimize the influence of normal anatomic variability of the brain in functional group analysis, such as functional mapping of the brain using functional magnetic resonance imaging (fMRI). They are also important in diagnostic medical imaging to develop automatic algorithms for the diagnosis of brain diseases. Modeling the shape of the brain is difficult because of the complexity of brain morphology and the large degree of variability in normal human brain structure. These difficulties raise the need for statistics and computational methods to analyze curves, surfaces, and deformations. In this section, voxel-based morphometry (VBM) and deformation-based morphometry (DBM) will be introduced.

 
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