The reconstruction of histology sections into a 3-D volume receives increased attention due to its various applications in modern medical image analysis. To guarantee a geometrically coherent reconstruction, we propose a new way to register histological sections simultaneously to previously acquired reference images and to neighboring slices in the stack. To this end, we formulate two potential functions and associate them to the same Markov random field through which we can efficiently find an optimal solution. Due to our simultaneous formulation and the absence of any segmentation step during the reconstruction we can dramatically reduce error propagation effects. This is illustrated by experiments on carefully created synthetic as well as real data sets. © 2011 Springer-Verlag.
CITATION STYLE
Feuerstein, M., Heibel, H., Gardiazabal, J., Navab, N., & Groher, M. (2011). Reconstruction of 3-D histology images by simultaneous deformable registration. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6892 LNCS, pp. 582–589). https://doi.org/10.1007/978-3-642-23629-7_71
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