Mapping Multi-Modal Routine Imaging Data to a Single Reference via Multiple Templates

  • Johannes Hofmanninger
  • Bjoern Menze
  • Marc-André Weber
  • Georg Langs
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10553)


Population level analysis of medical imaging data relies on finding spatial correspondence across individuals as a basis for local comparison of visual characteristics. Here, we describe and evaluate a framework to normalize routine images covering different parts of the human body, in different modalities to a common reference space. The framework performs two basic steps towards normalization: (1) The identification of the location and coverage of the human body by an image and (2) a non-linear mapping to the common reference space. Based on these mappings, either coordinates, or label-masks can be transferred across a population of images. We evaluate the framework on a set of routine CT and MR scans exhibiting large variability on location and coverage. A set of manually annotated landmarks is used to assess the accuracy and stability of the approach. We report distinct improvement in stability and registration accuracy compared to a classical single-atlas approach.


  1. 1.
    Crum, W.R., Griffin, L.D., Hill, D.L.G., Hawkes, D.J.: Zen and the art of medical image registration: correspondence, homology, and quality. NeuroImage 20(3), 1425–1437 (2003)CrossRefGoogle Scholar
  2. 2.
    Degen, J., Heinrich, M.P.: Multi-atlas based pseudo-CT synthesis using multimodal image registration and local atlas fusion strategies. In: Computer Vision and Pattern Recognition (CVPR), pp. 160–168 (2016)Google Scholar
  3. 3.
    Fischl, B., Salat, D.H., Busa, E., Albert, M., Dieterich, M., Haselgrove, C., van der Kouwe, A., Killiany, R., Kennedy, D., Klaveness, S., Montillo, A., Makris, N., Rosen, B., Dale, A.M.: Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron 33(3), 341–355 (2002)CrossRefGoogle Scholar
  4. 4.
    Goksel, O., Foncubierta-Rodriguez, A., del Toro, O.A.J., Müller, H., Langs, G., Weber, M.A., Menze, B.H., Eggel, I., Gruenberg, K., et al.: Overview of the VISCERAL challenge at ISBI 2015. In: VISCERAL Challenge@ ISBI, pp. 6–11 (2015)Google Scholar
  5. 5.
    Gruslys, A., Acosta-Cabronero, J., Nestor, P.J.: Others: a new fast accurate nonlinear medical image registration program including surface preserving regularization. IEEE Trans. Med. Imaging 33(11), 2118–2127 (2014)CrossRefGoogle Scholar
  6. 6.
    Hamm, J., Ye, D.H., Verma, R., Davatzikos, C.: GRAM: a framework for geodesic registration on anatomical manifolds. Med. Image Anal. 14(5), 633–642 (2010)CrossRefGoogle Scholar
  7. 7.
    Heinrich, M.P., Jenkinson, M., Papież, B.W., Brady, S.M., Schnabel, J.A.: Towards realtime multimodal fusion for image-guided interventions using self-similarities. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013. LNCS, vol. 8149, pp. 187–194. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-40811-3_24 CrossRefGoogle Scholar
  8. 8.
    Iglesias, J.E., Sabuncu, M.R.: Multi-atlas segmentation of biomedical images: a survey. Med. Image Anal. 24(1), 205–19 (2015)CrossRefGoogle Scholar
  9. 9.
    Koch, L.M., Rajchl, M., Bai, W., Baumgartner, C.F., Tong, T., Passerat-Palmbach, J., et al.: Multi-Atlas Segmentation using Partially Annotated Data: Methods and Annotation Strategies, pp. 1–17. arXiv preprint, arxiv:1605.00029 (2016)
  10. 10.
    Toews, M., Wells, W.M.: Efficient and robust model-to-image alignment using 3D scale-invariant features. Med. Image Anal. 17(3), 271–282 (2013)CrossRefGoogle Scholar
  11. 11.
    Viola, P., Wells Iii, W.M.: Alignment by maximization of mutual information. Int. J. Comput. Vis. 9(242), 22–137 (1997)Google Scholar
  12. 12.
    Wolz, R., Chu, C., Misawa, K., Fujiwara, M., Mori, K., Rueckert, D.: Automated abdominal multi-organ segmentation with subject-specific atlas generation. IEEE Trans. Med. Imaging 32(9), 1723–1730 (2013)CrossRefGoogle Scholar
  13. 13.
    Xie, L., Pluta, J.B., Das, S.R., Wisse, L.E., Wang, H., Mancuso, L., Kliot, D., Avants, B.B., Ding, S.L., Manjón, J.V., Wolk, D.A., Yushkevich, P.A.: Multi-template analysis of human perirhinal cortex in brain MRI: explicitly accounting for anatomical variability. NeuroImage 144, 183–202 (2017)CrossRefGoogle Scholar
  14. 14.
    Zhang, Y., Brady, M., Smith, S.: Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Trans. Med. Imaging 20(1), 45–57 (2001)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Johannes Hofmanninger
    • 1
  • Bjoern Menze
    • 2
  • Marc-André Weber
    • 3
    • 4
  • Georg Langs
    • 1
  1. 1.Department of Biomedical Imaging and Image-guided Therapy Computational Imaging Research LabMedical University of ViennaViennaAustria
  2. 2.Department of Computer Science & Institute for Advanced StudyTechnical University of MunichMunichGermany
  3. 3.Department of Diagnostic and Interventional RadiologyUniversity of HeidelbergHeidelbergGermany
  4. 4.Institute of Diagnostic and Interventional RadiologyRostock University Medical CenterRostockGermany

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