Automated 3D Analysis of Cortical Geometry in MR Images

MR 图像中皮质几何形状的自动 3D 分析

• 批准号: 7172280
• 负责人: Jerry L Prince
• 金额: $ 39.07万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 1998
• 资助国家: 美国
• 起止时间: 1998-08-20 至 2009-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7172280
• 关键词: Address; Age; Aging; Algorithms; Ally; Atlases; Biomechanics; Brain; Cerebral cortex; Computers; Correlation Studies; Cryoultramicrotomy; Data; Development; Disease; Effectiveness; Generations; Goals; Human; Hybrids; Image; Knowledge; Label; Magnetic Resonance; Magnetic Resonance Imaging; Manuals; Maps; Measures; Medical Imaging; Methodology; Methods; Metric; Modeling; Motor; Neurosciences; Numbers; Operative Surgical Procedures; Pattern; Physiological; Process; Research; Research Design; Resolution; Series; Shapes; Shipping; Ships; Structure; Structure-Activity Relationship; Surface; Techniques; Thick; Three-dimensional analysis; Today; association cortex; base; gray matter; image processing; in vivo; insight; neocortical; normal aging; novel; reconstruction; sensory cortex; white matter

Characterizing the relationship between the structure of the human brain and its 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. Despite significant recent advances, the current methodology is still limited by the lack of automatic methods for the detailed segmentation, geometric analysis, and labeling of the cerebral cortex. The major goals of the proposed research are to develop fully automated methods to find and mathematically represent the cerebral cortex in volumetric magnetic resonance (MR) images and to automatically identify and label the major sulci and gyri on the cortex using a detailed statistical analysis of cortical geometry. Specifically, we propose to 1) develop and validate methods to find and mathematically represent the cerebral cortex from volumetric MR images; 2) develop and validate methods to calculate regional measures of cortical shape and volume; 3) develop and validate automatic labelling of sulci and gryi; and 4) conduct studies of cortical variability and volume changes in normal aging. All methods will be extensively validated using both computer phantoms and manual in vivo truth models. The methods we develop to automatically represent and label 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.

描述人类大脑的结构和功能之间的关系是最重要的研究之一

项目成果

A Multiple Geometric Deformable Model Framework for Homeomorphic 3D Medical Image Segmentation.

用于同构三维医学图像分割的多重几何可变形模型框架。

• DOI: 10.1109/cvprw.2008.4563013
• 发表时间: 2008-07-15
• 期刊: Conference on Computer Vision and Pattern Recognition Workshops. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Workshops
• 作者: Fan X;Bazin PL;Bogovic J;Bai Y;Prince JL
• 通讯作者: Prince JL

MR CONTRAST SYNTHESIS FOR LESION SEGMENTATION.

• DOI: 10.1109/isbi.2010.5490140
• 发表时间: 2010-06-21
• 期刊: Proceedings. IEEE International Symposium on Biomedical Imaging
• 作者: Roy S;Carass A;Shiee N;Pham DL;Prince JL
• 通讯作者: Prince JL

AUTOMATED RELIABLE LABELING OF THE CORTICAL SURFACE.

皮质表面的自动可靠标记。

• DOI: 10.1109/isbi.2008.4541027
• 发表时间: 2008
• 期刊: Proceedings. IEEE International Symposium on Biomedical Imaging
• 作者: Wan,Jing;Carass,Aaron;Resnick,SusanM;Prince,JerryL
• 通讯作者: Prince,JerryL

Distance Transforms in Multi Channel MR Image Registration.

多通道 MR 图像配准中的距离变换。

• DOI: 10.1117/12.878367
• 发表时间: 2011
• 期刊: Proceedings of SPIE--the International Society for Optical Engineering
• 作者: Chen,Min;Carass,Aaron;Bogovic,John;Bazin,Pierre-Louis;Prince,JerryL
• 通讯作者: Prince,JerryL

Approaching expert results using a hierarchical cerebellum parcellation protocol for multiple inexpert human raters.

• DOI: 10.1016/j.neuroimage.2012.08.075
• 发表时间: 2013-01-01
• 期刊: NEUROIMAGE
• 影响因子: 5.7
• 作者: Bogovic, John A.;Jedynak, Bruno;Rigg, Rachel;Du, Annie;Landman, Bennett A.;Prince, Jerry L.;Ying, Sarah H.
• 通讯作者: Ying, Sarah H.