Characterizing the relationship between the structure of the human brain and it function is one of the most important goals in neuroscience today. Medical imaging has been used to gain significant new insights into this relationship through the use of both anatomical and physiological imaging methods. The current methodology, however, is limited by the lack of automatic methods for the segmentation, geometric analysis, and labeling of the cerebral cortex. The goals of the proposed research are to develop automated methods to find and mathematically represent the cerebral cortex in volumetric magnetic resonance (MR) images, and to identify and label the major sulci on the cortex using a detailed analysis of cortical geometry. Specifically, the applicants proposed to: 1) develop and validate methods to find and mathematically represent the central layer (approximately layer 4) of the cerebral cortex in volumetric MR images; 2) develop and validate methods to extract geometric quantities from the cortical surface; 3) develop and validate methods to automatically identify and label themajor sulci of the brain; and 4) conduct a pilot study of regional cortical volume and geometry changes in normal aging. All methods will be extensively validated using both computer phantoms and manual in vivo truth models. The methods to automatically analyze the geometry of the cortex in large numbers of subjects should also be useful in: 1) the development of a description of normal versus diseased cortical geometry; 2) automatic landmark generation for deformable atlas registration; 3) statistical correlation studies of structure/function relationships; and 4) the analysis of morphological changes in ontogenesis, phylogenesis, aging, and disease.
| Shiee, Navid; Bazin, Pierre-Louis; Cuzzocreo, Jennifer L et al. (2014) Reconstruction of the human cerebral cortex robust to white matter lesions: method and validation. Hum Brain Mapp 35:3385-401 |
| Bogovic, John A; Jedynak, Bruno; Rigg, Rachel et al. (2013) Approaching expert results using a hierarchical cerebellum parcellation protocol for multiple inexpert human raters. Neuroimage 64:616-29 |
| Roy, Snehashis; Carass, Aaron; Bazin, Pierre-Louis et al. (2012) Consistent segmentation using a Rician classifier. Med Image Anal 16:524-35 |
| Roy, Snehashis; Carass, Aaron; Prince, Jerry L (2011) COMPRESSED SENSING BASED INTENSITY NON-UNIFORMITY CORRECTION. Proc IEEE Int Symp Biomed Imaging 2011:101-104 |
| Carass, Aaron; Cuzzocreo, Jennifer; Wheeler, M Bryan et al. (2011) Simple paradigm for extra-cerebral tissue removal: algorithm and analysis. Neuroimage 56:1982-92 |
| Chen, Min; Carass, Aaron; Bogovic, John et al. (2011) Distance Transforms in Multi Channel MR Image Registration. Proc SPIE Int Soc Opt Eng 2011: |
| Shiee, Navid; Bazin, Pierre-Louis; Ozturk, Arzu et al. (2010) A topology-preserving approach to the segmentation of brain images with multiple sclerosis lesions. Neuroimage 49:1524-35 |
| Bogovic, John A; Landman, Bennett A; Bazin, Pierre-Louis et al. (2010) Statistical Fusion of Surface Labels Provided by Multiple Raters. Proc SPIE Int Soc Opt Eng 7623: |
| Lucas, Blake C; Bogovic, John A; Carass, Aaron et al. (2010) The Java Image Science Toolkit (JIST) for rapid prototyping and publishing of neuroimaging software. Neuroinformatics 8:5-17 |
| Roy, Snehashis; Carass, Aaron; Shiee, Navid et al. (2010) MR CONTRAST SYNTHESIS FOR LESION SEGMENTATION. Proc IEEE Int Symp Biomed Imaging 2010:932-935 |
Showing the most recent 10 out of 34 publications
