Deformable reconstruction of histology sections using structural probability maps
Müller M, Yigitsoy M, Heibel H, Navab N (2014)
Publication Type: Conference contribution
Publication year: 2014
Journal
Publisher: Springer Verlag
Book Volume: 8673 LNCS
Pages Range: 122-129
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: USA
ISBN: 9783319104034
DOI: 10.1007/978-3-319-10404-1_16
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. © 2014 Springer International Publishing.
Involved external institutions
Germany (DE)How to cite
APA:
Müller, M., Yigitsoy, M., Heibel, H., & Navab, N. (2014). Deformable reconstruction of histology sections using structural probability maps. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 122-129). USA: Springer Verlag.
MLA:
Müller, Markus, et al. "Deformable reconstruction of histology sections using structural probability maps." Proceedings of the 17th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014, USA Springer Verlag, 2014. 122-129.
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