Name
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. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/978-3-319-10404-1_16 | Lecture Notes in Computer Science |
Field | DocType | Volume |
Computer vision,Pattern recognition,Computer science,Artificial intelligence,Smoothness | Conference | 8673 |
Issue | ISSN | Citations |
Pt 1 | 0302-9743 | 3 |
PageRank | References | Authors |
0.40 | 8 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Markus Müller | 1 | 3 | 0.40 |
| Mehmet Yigitsoy | 2 | 74 | 8.54 |
| Hauke Heibel | 3 | 55 | 4.51 |
| Nassir Navab | 4 | 6594 | 578.60 |