A combined manifold learning analysis of shape and appearance to characterize neonatal brain development

IEEE Transactions on Medical Imaging

Volumn 30, Issue 12, 2011, Pages 2072-2086

  Aljabar, P ^a   Wolz, R ^a   Srinivasan, L ^b   Counsell, S J ^c   Rutherford, M A ^c   Edwards, A D ^b   Hajnal, J V ^c   Rueckert, D ^a  

^a IMPERIAL COLLEGE LONDON   (United Kingdom)

^b IMPERIAL COLLEGE HEALTHCARE NHS TRUST   (United Kingdom)

^c HAMMERSMITH HOSPITAL   (United Kingdom)

Author keywords

Dimensionality reduction; magnetic resonance (MR) images; manifold learning; neonatal brain development

Indexed keywords

BRAIN DEVELOPMENT; BRAIN MR; CLINICAL BIOMARKERS; CLINICAL DATA; CLINICAL STUDY; CORTICAL SURFACES; DIMENSIONALITY REDUCTION; EMBEDDINGS; IMAGE SIMILARITY; MANIFOLD LEARNING; MEDICAL IMAGES; MR IMAGES; MULTIPLE FEATURES; NEONATAL BRAIN DEVELOPMENT; NON-RIGID DEFORMATION; RAPID CHANGES;

DATA PROCESSING; IMAGE PROCESSING; MAGNETIC RESONANCE; MEDICAL IMAGING; MORPHOLOGY; PEDIATRICS; POPULATION STATISTICS;

BRAIN;

ALGORITHM; ARTICLE; AUTOMATED PATTERN RECOGNITION; BIOLOGICAL MODEL; BRAIN; FEMALE; GESTATIONAL AGE; GROWTH, DEVELOPMENT AND AGING; HISTOLOGY; HUMAN; IMAGE PROCESSING; MALE; METHODOLOGY; NEWBORN; NUCLEAR MAGNETIC RESONANCE IMAGING;

ALGORITHMS; BRAIN; FEMALE; GESTATIONAL AGE; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; INFANT, NEWBORN; MAGNETIC RESONANCE IMAGING; MALE; MODELS, BIOLOGICAL; PATTERN RECOGNITION, AUTOMATED;

EID: 82455221106     PISSN: 02780062     EISSN: None     Source Type: Journal    
DOI: 10.1109/TMI.2011.2162529     Document Type: Article

References (57)

1. J. Ashburner and K. J. Friston, "Voxel-based morphometry-The methods," NeuroImage, vol. 11, no. 6, pp. 805-821, 2000.
2. C. Good, I. Johnsrude, J. Ashburner, R. Henson, K. Friston, and R. Frackowiak, "A voxel-based morphometric study of ageing in 465 normal adult human brains," NeuroImage, vol. 14, no. 1, pp. 21-36, 2001. (Pubitemid 32552740)
3. M. Kubicki, M. E. Shenton, D. F. Salisbury, Y. Hirayasu, K. Kasai, R. Kikinis, F. A. Jolesz, and R. W. McCarley, "Voxel-based morphometric analysis of gray matter in first episode schizophrenia," NeuroImage, vol. 17, no. 4, pp. 1711-1719, 2002. (Pubitemid 36062762)
4. C. Gaser, I. Nenadoc, B. R. Buchsbaum, E. A. Hazlett, and M. S. Buchsbaum, "Deformation-based morphometry and its relation to conventional volumetry of brain lateral ventricles in MRI," NeuroImage, vol. 13, pp. 1140-1145, 2001. (Pubitemid 32553544)
5. T. Rohlfing, E. V. Sullivan, and A. Pfefferbaum, "Deformation-based brain morphometry to track the course of alcoholism: Differences between intra-subject and inter-subject analysis," Psychiatry Res.: Neuroimag., vol. 146, no. 2, pp. 157-170, 2006.
6. C. Studholme, "Incorporating DTI data as a constraint in deformation tensor morphometry between T1 MR images," in Inf. Process. Med. Imag.: Proc. 20th Int. Conf. (IPMI'07), 2007, vol. 4584, pp. 223-232.
7. Z. Lao, D. Shen, Z. Xue, B. Karacali, S. M. Resnick, and C. Davatzikos, "Morphological classification of brains via high-dimensional shape transformations and machine learning methods," NeuroImage, vol. 21, no. 1, pp. 46-57, 2004. (Pubitemid 38114995)
8. Y. Fan, N. Batmanghelich, C. M. Clark, and C. Davatzikos, "Spatial patterns of brain atrophy in MCI patients, identified via high-dimensional pattern classification, predict subsequent cognitive decline," NeuroImage, vol. 39, pp. 1731-1743, 2008. (Pubitemid 351179414)
9. S. Baloch and C. Davatzikos, "Morphological appearance manifolds in computational anatomy: Groupwise registration and morphological analysis," NeuroImage, vol. 45, no. 1, pp. S73-S85, 2009.
10. F. Shi, P. T. Yap, Y. Fan, J. H. Gilmore, W. Lin, and D. Shen, "Construction of multi-region-multi-reference atlases for neonatal brain MRI segmentation," NeuroImage, vol. 51, no. 2, pp. 684-693, 2010.
11. Y.Wang, Y. Fan, P. Bhatt, and C. Davatzikos, "High-dimensional pattern regression using machine learning: From medical images to continuous clinical variables," NeuroImage, vol. 50, no. 4, pp. 1519-1535, 2010.
12. L. van der Maaten, E. O. Postma, and H. van den Herik, Dimensionality reduction: Acomparative reviewTilburg Univ., The Netherlands, Tech. Rep. TiCC-TR 2009-005, 2009 [Online]. Available: http://homepage.tudelft.nl/19j49/ Publications-files/TR-Dimensiereduct ie.pdf
13. G. Langs and N. Paragios, "Modeling the structure of multivariate manifolds: Shape maps," in IEEE Proc. Conf. Comput. Vis. Pattern Recognit. (CVPR'08), Jun. 2008, pp. 1-8.
14. R. R. Coifman and S. Lafon, "Diffusion maps," Appl. Computat. Harmonic Anal., vol. 21, no. 1, pp. 5-30, 2006. (Pubitemid 43947619)
15. M. Belkin and P. Niyogi, "Laplacian eigenmaps for dimensionality reduction and data representation," Neural Computat., vol. 15, no. 6, pp. 1373-1396, 2003. (Pubitemid 37049796)
16. P. Aljabar, D. Rueckert, and W. R. Crum, "Automated morphological analysis of magnetic resonance brain imaging using spectral analysis," NeuroImage, vol. 43, no. 2, pp. 225-235, 2008.
17. S. Gerber, T. Tasdizen, P. T. Fletcher, S. Joshi, and R. Whitaker, "Manifold modeling for brain population analysis," Med. Image Anal., vol. 14, no. 5, pp. 643-653, 2010.
18. J. B. Tenenbaum, V. de Silva, and J. C. Langford, "A global geometric framework for nonlinear dimensionality reduction," Science, vol. 290, no. 5500, pp. 2319-2323, 2000. (Pubitemid 32041577)
19. J. Hamm, D. H. Ye, R. Verma, and C. Davatzikos, "GRAM: A framework for geodesic registration on anatomical manifolds," Med. Image Anal., vol. 14, no. 5, pp. 633-642, 2010.
20. R. Wolz, P. Aljabar, J. V. Hajnal, A. Hammers, and D. Rueckert, "LEAP: Learning embeddings for atlas propagation," NeuroImage, vol. 49, no. 2, pp. 1316-1325, 2010.
21. Z. Y. Sun, M. Perrot, A. Tucholka, D. Rivière, and J. F. Mangin, "Constructing a dictionary of human brain folding patterns," in Twelfth Int. Conf. on Medical Image Computing and Computer-Assisted Intervention (MICCAI'09). New York: Springer, vol. 5762, Lecture Notes in Computer Science, pp. 117-124.
22. S. Atasoy, D. Mateus, J. Lallemand, A. Meining, G. Z. Yang, and N. Navab, "Endoscopic video manifolds," in Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI'10). New York: Springer, 2010, vol. 6362, Lecture Notes in Computer Science, pp. 437-445.
23. C. Wachinger, M. Yigitsoy, and N. Navab, "Manifold learning for image-based breathing gating with application to 4D ultrasound," in Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI'10). New York: Springer, 2010, vol. 6362, Lecture Notes in Computer Science,, pp. 26-33.
24. R. Sparks and A. Madabhushi, "Novel morphometric based classification via diffeomorphic based shape representation using manifold learning," in Conference on Medical Image Computing and Computer- Assisted Intervention (MICCAI'10). New York: Springer, vol. 6363, Lecture Notes in Computer Science, pp. 658-665.
25. X. He, S. Yan, Y. Hu, P. Niyogi, and H. J. Zhang, "Face recognition using Laplacianfaces," IEEE Trans. Pattern Anal. Mach. Intell., vol. 27, no. 3, pp. 328-340, Mar. 2005. (Pubitemid 40377326)
26. J. Zhang, S. Li, and J. Wang, "Manifold learning and applications in recognition," Intelligent Multimedia Processing With Soft Computing ser. Studies in Fuzziness and Soft Computing, 2005, vol. 168, pp. 281-300.
27. F. Yun and T. S. Huang, "Human age estimation with regression on discriminative aging manifold," IEEE Trans. Multimedia, vol. 10, no. 4, pp. 578-584, Jun. 2008. (Pubitemid 351711234)
28. P. A. Habas, K. Kim, J. M. Corbett-Detig, F. Rousseau, O. A. Glenn, A. J. Barkovich, and C. Studholme, "A spatiotemporal atlas of MR intensity, tissue probability and shape of the fetal brain with application to segmentation," NeuroImage, vol. 53, no. 2, pp. 460-470, 2010.
29. M. Murgasova, P. Aljabar, L. Srinivasan, S. J. Counsell, V. Doria, A. Serag, I. S. Gousias, J. P. Boardman, M. A. Rutherford, A. D. Edwards, J. V. Hajnal, and D. Rueckert, "A dynamic 4D probabilistic atlas of the developing brain," NeuroImage, vol. 54, no. 4, pp. 2750-2763, 2011.
30. F. Shi, Y. Fan, S. Tang, J. H. Gilmore, W. Lin, and D. Shen, "Neonatal brain image segmentation in longitudinal MRI studies," NeuroImage, vol. 49, no. 1, pp. 391-400, 2010.
31. P. A. Habas, K. Kim, F. Rousseau, O. A. Glenn, A. J. Barkovich, and C. Studholme, "Atlas-based segmentation of developing tissues in the human brain with quantitative validation in young fetuses," Human Brain Mapp., vol. 31, no. 9, pp. 1348-1358, 2010.
32. M. Prastawa, N. Sadeghi, J. H. Gilmore, W. Lin, and G. Gerig, "A new framework for analyzing white matter maturation in early brain development," in IEEE Int. Symp. Biomed. Imag. (ISBI): From Nano to Macro, Apr. 2010, pp. 97-100.
33. , . : A. Serag, P. Aljabar, J. V. Hajnal, S. J. Counsell, J. P. Boardman, and D. Rueckert, "Tracking developmental changes in subcortical structures of the preterm brain using multi-modal MRI," in IEEE Int. Symp. Biomed. Imag. (ISBI): From Nano to Macro, 2011, pp. 349-352.
34. M. Sabuncu, S. Balci, M. Shenton, and P. Golland, "Discovering modes of an image population through mixture modeling," in Eleventh Int. Conf. on Medical Image Computing and Computer-Assisted Intervention (MICCAI'08). New York: Springer, 2008, vol. 5242, Lecture Notes in Computer Science, pp. 381-389.
35. D. Blezek and J. Miller, "Atlas stratification," Med. Image Anal., vol. 11, no. 5, pp. 443-457, 2007. (Pubitemid 47365760)
36. P. Aljabar, R. Wolz, L. Srinivasan, S. J. Counsell, J. P. Boardman, M. Murgasova, V. Doria, M. A. Rutherford, A. D. Edwards, J. V. Hajnal, and D. Rueckert, "Combining morphological information in a manifold learning framework: Application to neonatal MRI," in Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI'10). New York: Springer, 2010, vol. 6363, Lecture Notes in Computer Science, pp. 1-8.
37. M. Vaillant and J. Glaunés, "Surface matching via currents," in Information Processing in Medical Imaging: Proc. 19th International Conference (IPMI'05). New York: Springer, 2005, vol. 3565, Lecture Notes in Computer Science, pp. 381-392. (Pubitemid 41425331)
38. M. F. Beg, M. I. Miller, A. Trouvé, and L. Younes, "Computing large deformation metric mappings via geodesic flows of diffeomorphisms," Int. J. Comput. Vis., vol. 61, no. 2, pp. 139-157, 2005.
39. S. T. Roweis and L. K. Saul, "Nonlinear dimensionality reduction by locally linear embedding," Science, vol. 290, no. 5500, pp. 2323-2326, 2000. (Pubitemid 32041578)
40. L. Ha, M. Prastawa, G. Gerig, J. H. Gilmore, C. T. Silva, and S. Joshi, "Image registration driven by combined probabilistic and geometric descriptors," in Conference on Medical Image Computing and Computer- Assisted Intervention (MICCAI'10). New York: Springer, 2010, vol. 6362, Lecture Notes in Computer Science, pp. 602-609.
41. H. Xue, L. Srinivasan, S. Jiang, M. Rutherford, A. D. Edwards, D. Rueckert, and J. V. Hajnal, "Automatic segmentation and reconstruction of the cortex from neonatal MRI," NeuroImage, vol. 38, no. 3, pp. 461-477, 2007. (Pubitemid 47539905)
42. W. R. Crum, L. D. Griffin, D. L. G. Hill, and D. J. Hawkes, "Zen and the art of medical image registration: Correspondence, homology, and quality," NeuroImage, vol. 20, no. 3, pp. 1425-1437, 2003. (Pubitemid 37468253)
43. E. Haber and J. Modersitzki, "COFIR: Coarse and fine image registration," in Real-Time PDE-Constrained Optimization. Philadelphia, PA: SIAM, 2006, pp. 37-49.
44. W. Torgerson, "Multidimensional scaling: I. Theory and method," Psychometrika, vol. 17, pp. 401-419, 1952.
45. U. von Luxburg, "A tutorial on spectral clustering," Stat. Comput., vol. 17, no. 4, pp. 395-416, 2007.
46. J. S. Taylor and N. Cristianini, Eds., Kernel Methods for Pattern Analysis. Cambridge, U.K.: Cambridge Univ. Press, 2004.
47. J. G. Sled, A. P. Zijdenbos, and A. C. Evans, "A non-parametric method for automatic correction of intensity non-uniformity in MRI data," IEEE Trans. Med. Imag., vol. 17, no. 1, pp. 87-97, Feb. 1998. (Pubitemid 128742012)
48. S. M. Smith, "Fast robust automated brain extraction," Human Brain Mapp., vol. 17, no. 3, pp. 143-155, Nov. 2002. (Pubitemid 35253488)
49. D. Rueckert, L. I. Sonoda, C. Hayes, D. L. G. Hill, M. O. Leach, and D. J. Hawkes, "Non-rigid registration using free-form deformations: Application to breast MR images," IEEE Trans. Med. Imag., vol. 18, no. 8, pp. 712-721, Aug. 1999. (Pubitemid 32262025)
50. J. A. Schnabel, D. Rueckert, M. Quist, J. M. Blackall, A. D. Castellano- Smith, T. Hartkens, G. P. Penney, W. A. Hall, H. Liu, C. L. Truwit, F. A. Gerritsen, D. L. G. Hill, and D. J. Hawkes, "A generic framework for non-rigid registration based on non-uniform multi-level freeform deformations," in Fourth Int. Conf. on Medical Image Computing and Computer-Assisted Intervention (MICCAI'01), W. J. Niessen and M. A. Viergever, Eds. New York: Springer, 2001, vol. 2208, Lecture Notes in Computer Science, pp. 573-581. (Pubitemid 33352458)
51. J. Cohen and P. Cohen, Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences. Hillsdale, NJ: Lawrence Erlbaum, 1983.
52. T. J. Hutton, B. F. Buxton, P. Hammond, and H. Potts, "Estimating average growth trajectories in shape-space using kernel smoothing," IEEE Trans. Med. Imag., vol. 22, no. 6, pp. 747-753, Jun. 2003.
53. A. Ericsson, P. Aljabar, and D. Rueckert, "Construction of a patientspecific atlas of the brain: Application to normal aging," in IEEE Int. Symp. Biomed. Imag. (ISBI): From Nano to Macro, 2008, pp. 480-483.
54. R. Souvenir and R. Pless, "Image distance functions for manifold learning," Image Vis. Comput., vol. 25, no. 3, pp. 365-373, 2007. (Pubitemid 44880518)
55. J. J. Volpe, "The encephalopathy of prematurity-Brain injury and impaired brain development inextricably intertwined," Seminars Pediatric Neurol., vol. 16, no. 4, pp. 167-178, 2009.
56. G. Ball, P. J. Boardman, D. Rueckert, P. Aljabar, T. Arichi, N. Merchant, I. S. Gousias, A. D. Edwards, and S. J. Counsell, "The effect of preterm birth on thalamic and cortical development," Cerebral Cortex, 2011, to be published.
57. R. Wolz, P. Aljabar, J. V. Hajnal, J. Lötjönen, and D. Rueckert, "Improved manifold classification incorporating subject knowledge," in IEEE Int. Symp. Biomed. Imag. (ISBI): From Nano to Macro, 2011.