Regression-based label fusion for multi-atlas segmentation

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volumn , Issue , 2011, Pages 1113-1120

  Wang, Hongzhi ^a   Suh, Jung Wook ^a   Das, Sandhitsu ^a   Pluta, John ^a   Altinay, Murat ^a   Yushkevich, Paul ^a  

^a UNIVERSITY OF PENNSYLVANIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

IMAGE ENHANCEMENT; IMAGE SEGMENTATION; MAGNETIC RESONANCE; MAGNETIC RESONANCE IMAGING; MEDICAL IMAGING; PATTERN RECOGNITION;

AUTOMATIC SEGMENTATIONS; LABEL FUSIONS; MAGNETIC RESONANCE IMAGES (MRI); SEGMENTATION ERROR;

IMAGE FUSION;

EID: 80052889935     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2011.5995382     Document Type: Conference Paper

References (24)

1. S. Allassonniere, Y. Amit, and A. Trouve. Towards a coherent statistical framework for dense deformable template estimation. Journal of the Royal Statistical Society: Series B, 69(1):3-29, 2007.
2. X. Artaechevarria, A. Munoz-Barrutia, and C. O. de Solorzano. Combination strategies in multi-atlas image segmentation: Application to brain MR data. IEEE Tran. Medical Imaging, 28(8):1266-1277, 2009.
3. 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, 12:26-41, 2008.
4. D. Collins and J. Pruessner. Towards accurate, automatic segmentation of the hippocampus and amygdala from MRI by augmenting ANIMAL with a template library and label fusion. NeuroImage, 52(4):1355-1366, 2010.
5. P. Coupe, J. Manjon, V. Fonov, J. Pruessner, N. Robles, and D. Collins. Nonlocal patch-based label fusion for hippocampus segmentation. In MICCAI, 2010.
6. L. Dice. Measure of the amount of ecological association between species. Ecology, 26:297-302, 1945.
7. L. K. Hansen and P. Salamon. Neural network ensembles. IEEE Trans. on PAMI, 12(10):993-1001, 1990.
8. D. Hasboun, M. Chantome, A. Zouaoui, M. Sahel, M. Deladoeuille, N. Sourour, M. Duymes, M. Baulac, C. Marsault, and D. Dormont. MR determination of hippocampal volume: Comparison of three methods. Am J Neuroradiol, 17:1091-1098, 1996.
9. R. Heckemann, J. Hajnal, P. Aljabar, D. Rueckert, and A. Hammers. Automatic anatomical brain MRI segmentation combining label propagation and decision fusion. NeuroImage, 33:115-126, 2006.
10. T. K. Ho, J. Hull, and S. Srihari. Decision combination in multiple classifier systems. IEEE Trans. on Pattern Analysis and Machine Intelligence, 16(1):66-75, 1994.
11. S. Joshi, B. Davis, M. Jomier, and G. Gerig. Unbiased diffeomorphism atlas construction for computational anatomy. NeuroImage, 23:151-160, 2004.
12. J. Kittler. Combining classifiers: A theoretical framework. Pattern Analysis and Application, 1:18-27, 1998.
13. J. Kittler and F. Alkoot. Sum versus vote fusion in multiple classifier systems. IEEE Trans. on PAMI, 25(1):110-115, 2003.
14. A. Klein. Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration. NeuroImage, 46(3):786-802, 2009.
15. K. Leung, J. Barnes, G. Ridgway, J. Bartlett, M. Clarkson, K. Macdonald, N. Schuff, N. Fox, and S. Ourselin. Automated cross-sectional and longitudinal hippocampal volume measurement in mild cognitive impairment and Alzheimer's Disease. NeuroImage, 51:1345-1359, 2010.
16. O. Melnik, Y. Vardi, and C.-H. Zhang. Mixed group ranks: Preference and confidence in classifier combination. IEEE Trans. on PAMI, 26(8):973-981, 2004.
17. J. Pluta, B. Avants, S. Glynn, S. Awate, J. Gee, and J. Detre. Appearance and incomplete label matching for diffeomorphic template based hippocampus segmentation. Hippocampus, 19:565-571, 2009.
18. T. Rohlfing, R. Brandt, R. Menzel, and C. Maurer. Evaluation of atlas selection strategies for atlas-based image segmentation with application to confocal microscopy images of bee brains. NeuroImage, 21(4):1428-1442, 2004.
19. T. Rohlfing, R. Brandt, R. Menzel, D. B. Russakoff, and C. R. M. Jr. Quo Vadis, atlas-based segmentation? The Handbook of Medical Image AnalysisVolume III: Registration Models, pages 435-486, 2005.
20. S. Roweis and L. Saul. Nonlinear dimensionality reduction bu locally linear embedding. Science, 290:2323-2326, 2000.
21. M. Sabuncu, B. Yeo, K. V. Leemput, B. Fischl, and P. Golland. A generative model for image segmentation based on label fusion. IEEE Trans. on Medical Imaging, 29(10):1714-1720, 2010.
22. R. Scahill, J. Schott, J. Stevens, and M. R. N. Fox. Mapping the evolution of regional atrophy in Alzheimer's Disease: unbiased analysis of fluidregistered serial MRI. Proc. Natl. Acad. Sci. U. S. A., 99(7):4703-4707, 2002.
23. S. Warfield, K. Zou, and W. Wells. Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation. IEEE Trans. on Medical Imaging, 23(7):903-921, 2004.
24. S. Warfield, K. Zou, and W. Wells. Validation of image segmentation by estimating rater bias and variance. Philosophical Transactions of the Royal Society, 366(1874):2361-2375, 2008.