Abstract
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.
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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)
Cifor, A., Bai, L., Pitiot, A.: Smoothness-guided 3-d reconstruction of 2-d histological images. NeuroImage 56(1), 197–211 (2011)
Likar, B., Pernuš, F.: Registration of serial transverse sections of muscle fibers. Cytometry 37(2), 93–106 (1999)
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)
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)
Gaffling, S., Daum, V., Hornegger, J.: Landmark-constrained 3-D histological imaging: A morphology-preserving approach. In: VMV, pp. 309–316 (2011)
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)
Yigitsoy, M., Navab, N.: Structure propagation for image registration. IEEE Transactions on Medical Imaging 32(9), 1657–1670 (2013)
Medioni, G., Tang, C., Lee, M.: Tensor voting: Theory and applications. In: Proceedings of RFIA, Paris, France (2000)
King, B.: Range data analysis by free-space modeling and tensor voting. ProQuest (2008)
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)
Kolmogorov, V., Rother, C.: Minimizing nonsubmodular functions with graph cuts-a review. TPAMI 29(7), 1274–1279 (2007)
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)
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)
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Müller, M., Yigitsoy, M., Heibel, H., Navab, N. (2014). Deformable Reconstruction of Histology Sections Using Structural Probability Maps. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014. MICCAI 2014. Lecture Notes in Computer Science, vol 8673. Springer, Cham. https://doi.org/10.1007/978-3-319-10404-1_16
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DOI: https://doi.org/10.1007/978-3-319-10404-1_16
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