Accurate segmentation of brain images into 34 structures combining a non-stationary adaptive statistical atlas and a multi-atlas with applications to Alzheimer'S disease

Proceedings - International Symposium on Biomedical Imaging

Volumn , Issue , 2013, Pages 1202-1205

  Yan, Zhennan ^a   Zhang, Shaoting ^a   Liu, Xiaofeng ^b   Metaxas, Dimitris N ^a   Montillo, Albert ^b  

^a RUTGERS UNIVERSITY   (United States)

^b GE GLOBAL RESEARCH   (United States)

Author keywords

Alzheimer's; brain segmentation; Dirichlet distribution; EM; label fusion; MRF; statistical atlas

Indexed keywords

ALZHEIMER'S; BRAIN SEGMENTATION; DIRICHLET DISTRIBUTIONS; EM; LABEL FUSIONS; MRF; STATISTICAL ATLAS;

MAGNETIC RESONANCE IMAGING; MEDICAL IMAGING; NEURODEGENERATIVE DISEASES;

IMAGE SEGMENTATION;

EID: 84881657655     PISSN: 19457928     EISSN: 19458452     Source Type: Conference Proceeding    
DOI: 10.1109/ISBI.2013.6556696     Document Type: Conference Paper

References (17)

1. J. Ashburner and K. Friston. Unified segmentation. NeuroImage, 26:839-851, 2005. (Pubitemid 40825026)
2. B. Avants, C. Epstein, M. Grossman, and J. Gee. Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain. Medical image analysis, 12(1):26-41, 2008. (Pubitemid 351189163)
3. M. Cardoso, M. Clarkson, G. Ridgway, M. Modat, N. Fox, and S. Ourselin. Load: A locally adaptive cortical segmentation algorithm. NeuroImage, 56(3):1386-97, 2011.
4. K. Ellis et al. The australian imaging, biomarkers and lifestyle (AIBL) study of aging: methodology and baseline characteristics of 1112 individuals recruited for a longitudinal study of alzheimer's disease. Int Psychogeriatr, 21(04):672-687, 2009.
5. B. Fischl, D. Salat, E. Busa, M. Albert, et al. Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron, 33(3):341-355, 2002. (Pubitemid 34162530)
6. R. Heckemann, S. Keihaninejad, P. Aljabar, D. Rueckert, et al. Improving intersubject image registration using tissue-class information benefits robustness and accuracy ofmulti-atlas based anatomical segmentation. NeuroImage, 51(1):221-227, 2010.
7. J. Huang, S. Zhang, and D. N. Metaxas. Efficient mr image reconstruction for compressed mr imaging. Medical Image Analysis, 15(5):670-679, 2011.
8. R. Huang, V. Pavlovic, and D. Metaxas. A graphical model framework for coupling MRFs and deformable models. In CVPR, volume 2, pages 739-746, 2004.
9. J. Iglesias, C. Liu, P. Thompson, and Z. Tu. Robust brain extraction across datasets and comparison with publicly available methods. TMI, 30(9):1617-1634, 2011.
10. X. Liu, A. Montillo, E. Tan, and J. Schenck. iSTAPLE: Improved Label Fusion for Segmentation by Combining STAPLE with Image Intensity. In SPIE Medical Imaging, 2013.
11. M. Modat, G. Ridgway, Z. Taylor, M. Lehmann, J. Barnes, D. Hawkes, N. Fox, and S. Ourselin. Fast free-form deformation using graphics processing units. Comput. Methods Prog. Biomed., 98(3):278-284, June 2010.
12. F. Rousseau, P. Habas, and C. Studholme. A supervised patchbased approach for human brain labeling. TMI, 30(10), 2011.
13. N. Shiee, P. Bazin, J. Cuzzocreo, A. Blitz, and D. Pham. Segmentation of brain images using adaptive atlases with application to ventriculomegaly. In IPMI, pages 1-12, 2011.
14. K. Van Leemput, F. Maes, D. Vandermeulen, and P. Suetens. Automated model-based tissue classification of mr images of the brain. TMI, 18(10):897-908, 1999. (Pubitemid 32211105)
15. S.Warfield, K. Zou, andW.Wells. Simultaneous truth and performance level estimation (staple): an algorithm for the validation of image segmentation. TMI, 23(7):903-921, 2004.
16. W. Wells, III, W. Grimson, R. Kikinis, and F. Jolesz. Adaptive segmentation of MRI data. In CVRMed, pages 59-69, 1995.
17. S. Zhang, D. Metaxas, and et.al. 3D segmentation of rodent brain structures using hierarchical shape priors and deformable models. In MICCAI, pages 611-618, 2011.