Low-Rank to the Rescue – Atlas-Based Analyses in the Presence of Pathologies

  • Xiaoxiao Liu
  • Marc Niethammer
  • Roland Kwitt
  • Matthew McCormick
  • Stephen Aylward
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8675)


Low-rank image decomposition has the potential to address a broad range of challenges that routinely occur in clinical practice. Its novelty and utility in the context of atlas-based analysis stems from its ability to handle images containing large pathologies and large deformations. Potential applications include atlas-based tissue segmentation and unbiased atlas building from data containing pathologies. In this paper we present atlas-based tissue segmentation of MRI from patients with large pathologies. Specifically, a healthy brain atlas is registered with the low-rank components from the input MRIs, the low-rank components are then re-computed based on those registrations, and the process is then iteratively repeated. Preliminary evaluations are conducted using the brain tumor segmentation challenge data (BRATS ’12).


  1. 1.
    Chitphakdithai, N., Duncan, J.: Non-rigid registration with missing correspondences in preoperative and postresection brain images. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010, Part I. LNCS, vol. 6361, pp. 367–374. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  2. 2.
    Irimia, A., Wang, B., Aylward, S., Prastawa, M., Pace, D., Gerig, G., Hovda, D., Kikinis, R., Vespa, P., Van Horn, J.: Neuroimaging of structural pathology and connectomics in traumatic brain injury: Toward personalized outcome prediction. NeuroImage: Clinical 1, 1–17 (2012)CrossRefGoogle Scholar
  3. 3.
    Joshi, S., Davis, B., Jomier, M., Gerig, G.: Unbiased diffeomorphic atlas construction for computational anatomy. NeuroImage 23, 151–160 (2004)CrossRefGoogle Scholar
  4. 4.
    Lin, Z., Chen, M., Ma, Y.: The augmented lagrange multiplier method for exact recovery of corrupted low-rank matrices. arXiv preprint arXiv:1009.5055 (2010)Google Scholar
  5. 5.
    Niethammer, M., Hart, G., Pace, D., Vespa, P., Irimia, A., Van Horn, J., Aylward, S.: Geometric metamorphosis. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part II. LNCS, vol. 6892, pp. 639–646. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  6. 6.
    Peng, Y., Ganesh, A., Wright, J., Xu, W., Ma, Y.: RASL: Robust alignment by sparse and low-rank decomposition for linearly correlated images. TPAMI 34(11), 2233–2246 (2012)CrossRefGoogle Scholar
  7. 7.
    Prastawa, M., Bullitt, E., Gerig, G.: Simulation of brain tumors in MR images for evaluation of segmentation efficacy. Med. Image Anal. 13(2), 297–311 (2009)CrossRefGoogle Scholar
  8. 8.
    Rohlfing, T., Zahr, N.M., Sullivan, E.V., Pfefferbaum, A.: The SRI24 multichannel atlas of normal adult human brain structure. Hum. Brain Mapp. 31(5), 798–819 (2010)CrossRefGoogle Scholar
  9. 9.
    Wang, B., Prastawa, M., Awate, S., Irimia, A., Chambers, M., Vespa, P., Van Horn, J., Gerig, G.: Segmentation of serial MRI of TBI patients using personalized atlas construction and topological change estimation. In: ISBI (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Xiaoxiao Liu
    • 1
  • Marc Niethammer
    • 2
  • Roland Kwitt
    • 3
  • Matthew McCormick
    • 1
  • Stephen Aylward
    • 1
  1. 1.Kitware Inc.USA
  2. 2.University of North Carolina at Chapel HillUSA
  3. 3.Department of Computer ScienceUniversity of SalzburgAustria

Personalised recommendations