Shape Analysis on Subcortical Structures with its Application in Children with Autism

摘要: Template-based shape analysis has been widely used to assess structural shape abnormalities (volume loss and its location) in a variety of neurodegenerative and neuropsychiatric diseases. Thus far, most existing morphometric shape analysis has largely focused on a single structure, such as the hippocampus, or thalamus. Nevertheless, there is considerable morphological variation in multiple structures in neural circuits across disease population. The assessment of the degree and pattern of multiple structural shapes is necessary to optimally distinguish subjects with early forms of various neuropsychiatric diseases. We introduce an automatic shape analysis procedure through large deformation diffeomorphic metric mapping (LDDMM) that generates subcortical template, segments the structures from raw MR images, quantifies the shape variation of each individual subject relative to the template, as well as makes statistical inference on covariance of the shape variations of multiple subcortical structures [1].