, Volume 11, Issue 1, pp 91–103 | Cite as

System for Integrated Neuroimaging Analysis and Processing of Structure

  • Bennett A. Landman
  • John A. Bogovic
  • Aaron Carass
  • Min Chen
  • Snehashis Roy
  • Navid Shiee
  • Zhen Yang
  • Bhaskar Kishore
  • Dzung Pham
  • Pierre-Louis Bazin
  • Susan M. Resnick
  • Jerry L. Prince
Original Article


Mapping brain structure in relation to neurological development, function, plasticity, and disease is widely considered to be one of the most essential challenges for opening new lines of neuro-scientific inquiry. Recent developments with MRI analysis of structural connectivity, anatomical brain segmentation, cortical surface parcellation, and functional imaging have yielded fantastic advances in our ability to probe the neurological structure-function relationship in vivo. To date, the image analysis efforts in each of these areas have typically focused on a single modality. Here, we extend the cortical reconstruction using implicit surface evolution (CRUISE) methodology to perform efficient, consistent, and topologically correct analyses in a natively multi-parametric manner. This effort combines and extends state-of-the-art techniques to simultaneously consider and analyze structural and diffusion information alongside quantitative and functional imaging data. Robust and consistent estimates of the cortical surface extraction, cortical labeling, diffusion-inferred contrasts, diffusion tractography, and subcortical parcellation are demonstrated in a scan-rescan paradigm. Accompanying this demonstration, we present a fully automated software system complete with validation data.


Brain MRI Cortical surface White matter parcellation Fiber tracking Sub-cortical segmentation 



The authors are appreciative of the careful feedback from the anonymous reviewers and editor (Dr. David Kennedy). This research was supported by NIH/NIA N01-AG-4-0012, NINDS 5R01NS070906, 1R03EB012461, 1R01NS056307, NIH/NIDAK25DA025356 (Bazin), and NIH/NINDSR01NS054255 (Pham).

Grant Support

J. L. Prince/B. A. Landman (subcontract): NIH/NIA N01-AG-4-0012, J. Prince: 1R01NS056307, B. Landman 1R03EB012461.


  1. Andersson, J. L. R., Skare, S., & Ashburner, J. (2003). How to correct susceptibility distortions in spin-echo echo-planar images: application to diffusion tensor imaging. NeuroImage, 20, 870–888.PubMedCrossRefGoogle Scholar
  2. Ashburner, J., & Friston, K. J. (2000). Voxel-based morphometry–the methods. NeuroImage, 11(6 Pt 1), 805–821.PubMedCrossRefGoogle Scholar
  3. Awate, S.P. and Gee, J.C. (2007). A fuzzy, nonparametric segmentation framework for DTI and MRI analysis, In Proceedings of the International Conference on Information Processing in Medical Imaging 2007 (IPMI’07). Kerkrade.Google Scholar
  4. Bazin, P. L., & Pham, D. L. (2007a). Topology-preserving tissue classification of magnetic resonance brain images. IEEE Transactions on Medical Imaging, 26(4), 487–496.PubMedCrossRefGoogle Scholar
  5. Bazin, P. L., & Pham, D. L. (2007b). Topology correction of segmented medical images using a fast marching algorithm. Computer Methods and Programs in Biomedicine, 88(2), 182–190.PubMedCrossRefGoogle Scholar
  6. Bazin, P.-L., & Pham, D. L. (2008). Homeomorphic brain image segmentation with topological and statistical atlases. Medical Image Analysis, 12, 616–625.PubMedCrossRefGoogle Scholar
  7. Bazin, P. L., et al. (2011). Direct segmentation of the major white matter tracts in diffusion tensor images. NeuroImage, 58(2), 458–468.PubMedCrossRefGoogle Scholar
  8. Beg, F., Miller, M., Trouve, A., & Younes. (2005). Computing large deformation metric mappings via geodesic flows of diffeomorphisms. International Journal of Computer Vision, 23(2), 108–118.Google Scholar
  9. Bogovic, J., et al. (2010). Statistical fusion of surface labels provided by multiple raters, over-complete, and ancillary data. In SPIE Medical Imaging Conference. San Diego, CA.Google Scholar
  10. Brain Innovation B.V. (2007) BrainVoyager. Available from:
  11. Carass, A., et al. (2011). Simple paradigm for extra-cerebral tissue removal: algorithm and analysis. NeuroImage, 56(4), 1982–1992.PubMedCrossRefGoogle Scholar
  12. Chen, M., et al. (2010). Multi-channel enhancement of the adaptive bases algorithm, In Organization for Human Brain Mapping. Barcelona, Spain.Google Scholar
  13. Cointepas, Y., et al. (2001). BrainVISA: Software platform for visualization and analysis of multi-modality brain data. NeuroImage, 13(6), S98.CrossRefGoogle Scholar
  14. Covington, K. et al. (2010). Interfaces and integration of medical image analysis frameworks: Challenges and Opportunities. In Biomedical Science and Engineering Conference. Oak Ridge, TN.Google Scholar
  15. Covington, K., Welch, E.B. and Landman, B.A. (2011). Integrating medical imaging analyses through a high-throughput bundled resource imaging system. In SPIE Medical Imaging Conference. Lake Buena Vista, Florida.Google Scholar
  16. Dale, A. M., Fischl, B., & Sereno, M. I. (1999). Cortical surface-based analysis. I. Segmentation and surface reconstruction. Neuroimage, 9(2), 179–194.PubMedCrossRefGoogle Scholar
  17. Desikan, R. S., et al. (2006). An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. NeuroImage, 31(3), 968–980.PubMedCrossRefGoogle Scholar
  18. Eckstein, I., et al. (2009). Active fibers: Matching deformable tract templates to diffusion tensor images. Neuroimage, 47(Supplement 2), T82–T89.PubMedCrossRefGoogle Scholar
  19. El Kouby, V., et al. (2005). MR Diffusion-based inference of a fiber bundle model from a population of subjects, In Proceedings of the 8th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI’05). Palm Springs.Google Scholar
  20. Fischl, B., Sereno, M. I., & Dale, A. M. (1999). Cortical surface-based analysis. II: Inflation, flattening, and a surface-based coordinate system. NeuroImage, 9(2), 195–207.PubMedCrossRefGoogle Scholar
  21. Friston, K., (2006) Statistical parametric mapping: The analysis of functional brain images (p. 656). Academic Press.Google Scholar
  22. Han, X., et al. (2001). Cortical surface reconstruction using a topology preserving geometric deformable model. In 5th IEEE Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA2001). Kauai, Hawaii.Google Scholar
  23. Han, X., Xu, C., & Prince, J. L. (2003). A topology preserving level set method for geometric deformable models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25, 755–768.CrossRefGoogle Scholar
  24. Han, X., et al. (2004). CRUISE: cortical reconstruction using implicit surface evolution. NeuroImage, 23(3), 997–1012.PubMedCrossRefGoogle Scholar
  25. Jezzard, P., & Balaban, R. S. (1995). Correction for geometric distortion in echo planar images from B0 field variations. Magnetic Resonance in Medicine, 34, 65–73.PubMedCrossRefGoogle Scholar
  26. Jezzard, P., Barnett, A. S., & Pierpaoli, C. (1998). Characterization and correction for Eddy current artifacts in echo planar diffusion imaging. Magnetic Resonance in Medicine, 39, 801–812.PubMedCrossRefGoogle Scholar
  27. Jones, D. K. (2008). Studying connections in the living human brain with diffusion MRI. Cortex, 44(8), 936–952.PubMedCrossRefGoogle Scholar
  28. Koenderink, J. J., & van Doorn, A. J. (1992). Surface shape and curvature scales. Image and Vision Computing, 10(8), 557–565.CrossRefGoogle Scholar
  29. Landman, B. A., et al. (2007). Effects of diffusion weighting schemes on the reproducibility of DTI-derived fractional anisotropy, mean diffusivity, and principal eigenvector measurements at 1.5T. Neuroimage, 36(4), 1123–1138.PubMedCrossRefGoogle Scholar
  30. Landman, B., et al. (2011). Multi-parametric neuroimaging reproducibility: A 3T resource study. NeuroImage, 4(14), 2854–2866.CrossRefGoogle Scholar
  31. Lawes, I. N. C., et al. (2008). Atlas-based segmentation of white matter tracts of the human brain using diffusion tensor tractography and comparison with classical dissection. NeuroImage, 39, 62–79.PubMedCrossRefGoogle Scholar
  32. Lenglet, C., Rousson, M., & Deriche, R. (2006). DTI segmentation by statistical surface evolution. IEEE Transactions on Medical Imaging, 25(6), 685–700.PubMedCrossRefGoogle Scholar
  33. Lucas, B. C., et al. (2010). The Java Image Science Toolkit (JIST) for rapid prototyping and publishing of neuroimaging software. Neuroinformatics, 8(1), 5–17.PubMedCrossRefGoogle Scholar
  34. MacDonald, D., et al. (2000). Automated 3-D extraction of inner and outer surfaces of cerebral cortex from MRI. NeuroImage, 12(3), 340–356.PubMedCrossRefGoogle Scholar
  35. Maddah, M., et al. (2007). Probabilistic clustering and quantitative analysis of white matter fiber tracts, In Proceedings of the International Conference on Information Processing in Medical Imaging 2007 (IPMI’07). Kerkrade.Google Scholar
  36. Maddah, M. et al. (2008). A mathematical framework for incorporating anatomical knowledge in DT-MRI analysis, In Biomedical Imaging: From Nano to Macro, 2008. ISBI 2008. 5th IEEE International Symposium on Biomedical Imaging (p. 105–108). Paris, France.Google Scholar
  37. Maes, F., et al. (1997). Multimodality image registration by maximization of mutual information. IEEE Transactions on Medical Imaging, 16(2), 187–198.PubMedCrossRefGoogle Scholar
  38. Mangin, J.-F., et al. (1995). From 3D magnetic resonance images to structural representations of the cortex topography using topology preserving deformations. Journal of Mathematical Imaging and Vision, 5, 297–318.CrossRefGoogle Scholar
  39. Marcus, D., et al. (2007). Open Access Series of Imaging Studies (OASIS): cross-sectional MRI data in young, middle aged, nondemented, and demented older adults. Journal of Cognitive Neuroscience, 19(9), 1498–1507.PubMedCrossRefGoogle Scholar
  40. Mori, S., et al. (1999). Three-dimensional tracking of axonal projections in the brain by magnetic resonance imaging. Annals of Neurology, 45(2), 265–269.PubMedCrossRefGoogle Scholar
  41. Mori, S. et al. (2005). MRI Atlas of human white matter. Elsevier.Google Scholar
  42. Mueller, S. G., et al. (2005). Ways toward an early diagnosis in Alzheimer’s disease: the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Alzheimer’s & Dementia, 1(1), 55–66.CrossRefGoogle Scholar
  43. Pechura, C.M. and Martin, J.B. (1991). Mapping the brain and its functions. integrating enabling technologies into neuroscience research. National Academy Press.Google Scholar
  44. Pham, D. L., & Prince, J. L. (1999). Adaptive fuzzy segmentation of magnetic resonance images. IEEE Transactions on Medical Imaging, 18(9), 737–752.PubMedCrossRefGoogle Scholar
  45. Pieper, S., et al. (2006). The NA-MIC Kit: ITK, VTK, pipelines, grids and 3D slicer as an open platform for the medical image computing community. in 3rd IEEE International Symposium on Biomedical Imaging: Nano to Macro.Google Scholar
  46. Riviere, D., et al. (2002). Automatic recognition of cortical sulci of the human brain using a congregation of neural networks. Medical Image Analysis, 6(2), 77–92.PubMedCrossRefGoogle Scholar
  47. Rohde, G. K., Aldroubi, A., & Dawant, B. M. (2003). The adaptive bases algorithm for intensity-based nonrigid image registration. IEEE Transactions on Medical Imaging, 22(11), 1470–1479.PubMedCrossRefGoogle Scholar
  48. Rohde, G. K., et al. (2004). Comprehensive approach for correction of motion and distortion in diffusion-weighted MRI. Magnetic Resonance in Medicine, 51, 103–114.PubMedCrossRefGoogle Scholar
  49. Sethian, J. A. (1999). Level Set Methods and Fast Marching Methods. Vol. second ed. Cambridge: Cambridge Univ. Press.Google Scholar
  50. Shattuck, D. W., & Leahy, R. M. (2000). BrainSuite: An automated corticalsurface identification tool. in Lecture Notes in Computer Science. Medical Image Computing and Computer-Assisted Intervention MICCAI. Berlin: Springer.Google Scholar
  51. Shiee, N., et al. (2008). Automated reconstruction of the cerebral cortex in multiple. In Sixth IEEE International Symposium on Biomedical Imaging (ISBI).Google Scholar
  52. Shiee, N., et al. (2010). A Topology-preserving approach to the segmentation of brain images with multiple sclerosis lesions. NeuroImage, 1524–1535.Google Scholar
  53. Smith, S. M., et al. (2004). Advances in functional and structural MR image analysis and implementation as FSL. NeuroImage, 23(Suppl 1), S208–S219.PubMedCrossRefGoogle Scholar
  54. Sporns, O., et al. (2004). Organization, development and function of complex brain networks. Trends in Cognitive Science, 8(9), 418–425.CrossRefGoogle Scholar
  55. Thambisetty, M., et al. (2010). Longitudinal changes in cortical thickness associated with normal aging. NeuroImage, 52(4), 1215–1223.PubMedCrossRefGoogle Scholar
  56. Thompson, P. M., & Toga, A. W. (2002). A framework for computational anatomy. Computing and Visualization in Science, 5, 1–12.CrossRefGoogle Scholar
  57. Tosun, D., Rettmann, M.E. and Prince, J.L. (2003). Mapping techniques for aligning sulci across multiple brains, in Proceedings of The Sixth Annual International Conference on Medical Image Computing and Computer-Assisted Interventions(MICCAI). Montréal.Google Scholar
  58. Tosun, D., Rettmann, M., & Prince, J. (2004). Mapping techniques for aligning sulci across multiple brains. Medical Image Analysis, 8(3), 295–309.PubMedCrossRefGoogle Scholar
  59. Van Essen, D. C., et al. (2001). An integrated software suite for surface-based analyses of cerebral cortex. Journal of the American Medical Informatics Association, 8(5), 443–459.PubMedCrossRefGoogle Scholar
  60. van Leemput, K., et al. (1999). Automated model-based tissue classification of MR images of the brain. IEEE Transactions on Medical Imaging, 18(10), 897–908.PubMedCrossRefGoogle Scholar
  61. Viola, P. and Wells, W.M. (1995). Alignment by maximization of mutual information. In Proc. 5th Int. Conf. on Computer Vision.Google Scholar
  62. Wakana, S., et al. (2007). Reproducibility of quantitative tractography methods applied to cerebral white matter. NeuroImage, 36, 630–644.PubMedCrossRefGoogle Scholar
  63. Warfield, S. K., Zou, K. H., & Wells, W. M. (2004). Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation. IEEE Transactions on Medical Imaging, 23(7), 903–921.PubMedCrossRefGoogle Scholar
  64. Xu, C., et al. (1999). Reconstruction of the human cerebral cortex from magnetic resonance images. IEEE Transactions on Medical Imaging, 18(6), 467–480.PubMedCrossRefGoogle Scholar
  65. Zeng, X., et al. (1999). Segmentation and measurement of the cortex from 3-D MR images using coupled-surfaces propagation. IEEE Transactions on Medical Imaging, 18(10), 927–937.PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  • Bennett A. Landman
    • 1
    • 2
    • 3
  • John A. Bogovic
    • 4
  • Aaron Carass
    • 4
  • Min Chen
    • 4
  • Snehashis Roy
    • 4
  • Navid Shiee
    • 4
  • Zhen Yang
    • 4
  • Bhaskar Kishore
    • 5
  • Dzung Pham
    • 4
    • 5
    • 6
  • Pierre-Louis Bazin
    • 8
  • Susan M. Resnick
    • 7
  • Jerry L. Prince
    • 2
    • 4
    • 5
  1. 1.Department of Electrical EngineeringVanderbilt UniversityNashvilleUSA
  2. 2.Department of Biomedical EngineeringJohns Hopkins UniversityBaltimoreUSA
  3. 3.The Department of Radiology and Radiological SciencesVanderbilt UniversityNashvilleUSA
  4. 4.Department of Electrical and Computer EngineeringJohns Hopkins UniversityBaltimoreUSA
  5. 5.The Russell H. Morgan Department of Radiology and Radiological SciencesJohns Hopkins University School of MedicineBaltimoreUSA
  6. 6.Center for Neuroscience and Regenerative MedicineWashingtonUSA
  7. 7.Laboratory of Personality and CognitionNational Institute on AgingBaltimoreUSA
  8. 8.Department of NeurophysicsMax Plank Institute for Human Cognitive and Brain SciencesLeipzigGermany

Personalised recommendations