title: Deformable reconstruction of histology sections using structural probability maps.
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.
authors.forename: Markus
authors.initials: M
authors.lastname: Müller
authors.forename: Mehmet
authors.initials: M
authors.lastname: Yigitsoy
authors.forename: Hauke
authors.initials: H
authors.lastname: Heibel
authors.forename: Nassir
authors.initials: N
authors.lastname: Navab
mesh_terms: D000465:Algorithms; D000818:Animals; D001921:Brain / Q000166:cytology*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D051379:Mice; D008853:Microscopy / Q000379:methods*; D015233:Models, Statistical; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity