Surface-based method to evaluate global brain shape asymmetries in human and chimpanzee brains

International audienceIn this paper we use humans and chimpanzees brain MRI databases to develop methods for evaluating global brain asymmetries. We perform brain segmentation and hemispheric surface extraction on both populations. The human brain segmentation pipeline is adapted to chimpanzees in order to obtain results of good quality. To alleviate the problems due to cortical variability we propose a mesh processing algorithm to compute the brain global shape. Surface-based global brain asymmetries are computed on chimpanzee and human subjects using individual mid-sagittal plane evaluation and population-level mean shape estimation. Asymmetry results are presented in terms of axis-wise components in order to perform more specific evaluation and comparison between the two populations

Surface-based method to evaluate global brain shape asymmetries in human and chimpanzee brains

International audienceIn this paper we use humans and chimpanzees brain MRI databases to develop methods for evaluating global brain asymmetries. We perform brain segmentation and hemispheric surface extraction on both populations. The human brain segmentation pipeline is adapted to chimpanzees in order to obtain results of good quality. To alleviate the problems due to cortical variability we propose a mesh processing algorithm to compute the brain global shape. Surface-based global brain asymmetries are computed on chimpanzee and human subjects using individual mid-sagittal plane evaluation and population-level mean shape estimation. Asymmetry results are presented in terms of axis-wise components in order to perform more specific evaluation and comparison between the two populations

Shape analysis of the human brain.

Autism is a complex developmental disability that has dramatically increased in prevalence, having a decisive impact on the health and behavior of children. Methods used to detect and recommend therapies have been much debated in the medical community because of the subjective nature of diagnosing autism. In order to provide an alternative method for understanding autism, the current work has developed a 3-dimensional state-of-the-art shape based analysis of the human brain to aid in creating more accurate diagnostic assessments and guided risk analyses for individuals with neurological conditions, such as autism. Methods: The aim of this work was to assess whether the shape of the human brain can be used as a reliable source of information for determining whether an individual will be diagnosed with autism. The study was conducted using multi-center databases of magnetic resonance images of the human brain. The subjects in the databases were analyzed using a series of algorithms consisting of bias correction, skull stripping, multi-label brain segmentation, 3-dimensional mesh construction, spherical harmonic decomposition, registration, and classification. The software algorithms were developed as an original contribution of this dissertation in collaboration with the BioImaging Laboratory at the University of Louisville Speed School of Engineering. The classification of each subject was used to construct diagnoses and therapeutic risk assessments for each patient. Results: A reliable metric for making neurological diagnoses and constructing therapeutic risk assessment for individuals has been identified. The metric was explored in populations of individuals having autism spectrum disorders, dyslexia, Alzheimers disease, and lung cancer. Conclusion: Currently, the clinical applicability and benefits of the proposed software approach are being discussed by the broader community of doctors, therapists, and parents for use in improving current methods by which autism spectrum disorders are diagnosed and understood