Deformable Reconstruction of Histology Sections Using Structural Probability Maps

  • Markus Müller
  • Mehmet Yigitsoy
  • Hauke Heibel
  • Nassir Navab
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8673)


The reconstruction of a 3D volume from a stack of 2D histology slices is still a challenging problem especially if no external references are available. Without a reference, standard registration approaches tend to align structures that should not be perfectly aligned. In this work we introduce a deformable, reference-free reconstruction method that uses an internal structural probability map (SPM) to regularize a free-form deformation. The SPM gives an estimate of the original 3D structure of the sample from the misaligned and possibly corrupted 2D slices. We present a consecutive as well as a simultaneous reconstruction approach that incorporates this estimate in a deformable registration framework. Experiments on synthetic and mouse brain datasets indicate that our method produces similar results compared to reference-based techniques on synthetic datasets. Moreover, it improves the smoothness of the reconstruction compared to standard registration techniques on real data.


  1. 1.
    Malandain, G., Bardinet, E., Nelissen, K., Vanduffel, W.: Fusion of autoradiographs with an mr volume using 2-d and 3-d linear transformations. NeuroImage 23(1), 111–127 (2004)CrossRefGoogle Scholar
  2. 2.
    Cifor, A., Bai, L., Pitiot, A.: Smoothness-guided 3-d reconstruction of 2-d histological images. NeuroImage 56(1), 197–211 (2011)CrossRefGoogle Scholar
  3. 3.
    Likar, B., Pernuš, F.: Registration of serial transverse sections of muscle fibers. Cytometry 37(2), 93–106 (1999)CrossRefGoogle Scholar
  4. 4.
    Bardinet, E., Ourselin, S., Dormont, D., Malandain, G., Tandé, D., Parain, K., Ayache, N., Yelnik, J.: Co-registration of histological, optical and mr data of the human brain. In: Dohi, T., Kikinis, R. (eds.) MICCAI 2002, Part I. LNCS, vol. 2488, pp. 548–555. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  5. 5.
    Feuerstein, M., Heibel, H., Gardiazabal, J., Navab, N., Groher, M.: Reconstruction of 3-d histology images by simultaneous deformable registration. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part II. LNCS, vol. 6892, pp. 582–589. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  6. 6.
    Gaffling, S., Daum, V., Hornegger, J.: Landmark-constrained 3-D histological imaging: A morphology-preserving approach. In: VMV, pp. 309–316 (2011)Google Scholar
  7. 7.
    Schwier, M., Böhler, T., Hahn, H.K., Dahmen, U., Dirsch, O.: Registration of histological whole slide images guided by vessel structures. Journal of Pathology Informatics 4(suppl.) (2013)Google Scholar
  8. 8.
    Yigitsoy, M., Navab, N.: Structure propagation for image registration. IEEE Transactions on Medical Imaging 32(9), 1657–1670 (2013)CrossRefGoogle Scholar
  9. 9.
    Medioni, G., Tang, C., Lee, M.: Tensor voting: Theory and applications. In: Proceedings of RFIA, Paris, France (2000)Google Scholar
  10. 10.
    King, B.: Range data analysis by free-space modeling and tensor voting. ProQuest (2008)Google Scholar
  11. 11.
    Glocker, B., Komodakis, N., Tziritas, G., Navab, N., Paragios, N.: Dense image registration through mrfs and efficient linear programming. Medical Image Analysis 12(6), 731–741 (2008)CrossRefGoogle Scholar
  12. 12.
    Kolmogorov, V., Rother, C.: Minimizing nonsubmodular functions with graph cuts-a review. TPAMI 29(7), 1274–1279 (2007)CrossRefGoogle Scholar
  13. 13.
    Baker, S., Scharstein, D., Lewis, J., Roth, S., Black, M.J., Szeliski, R.: A database and evaluation methodology for optical flow. IJCV 92(1), 1–31 (2011)CrossRefGoogle Scholar
  14. 14.
    Ju, T., Warren, J., Carson, J., Bello, M., Kakadiaris, I., Chiu, W., Thaller, C., Eichele, G.: 3d volume reconstruction of a mouse brain from histological sections using warp filtering. Journal of Neuroscience Methods 156(1), 84–100 (2006)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Markus Müller
    • 1
  • Mehmet Yigitsoy
    • 1
  • Hauke Heibel
    • 3
  • Nassir Navab
    • 1
    • 2
  1. 1.Computer Aided Medical ProceduresTechnische Universität MünchenGermany
  2. 2.Computer Aided Medical ProceduresJohns Hopkins UniversityUSA
  3. 3.microDimensions GmbHMünchenGermany

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