A Gauss-Seidel Iteration Scheme for Reference-Free 3-D Histological Image Reconstruction

Journal article


Publication Details

Author(s): Gaffling S, Daum V, Steidl S, Maier A, Köstler H, Hornegger J
Journal: IEEE Transactions on Medical Imaging
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Publication year: 2015
Volume: 34/2015
Journal issue: 2
Pages range: 1-17
ISSN: 0278-0062


Abstract


Three-dimensional (3-D) reconstruction of histological slice sequences offers great benefits in the investigation of different morphologies. It features very high-resolution which is still unmatched by in vivo 3-D imaging modalities, and tissue staining further enhances visibility and contrast. One important step during reconstruction is the reversal of slice deformations introduced during histological slice preparation, a process also called image unwarping. Most methods use an external reference, or rely on conservative stopping criteria during the unwarping optimization to prevent straightening of naturally curved morphology. Our approach shows that the problem of unwarping is based on the superposition of low-frequency anatomy and high-frequency errors. We present an iterative scheme that transfers the ideas of the Gauss-Seidel method to image stacks to separate the anatomy from the deformation. In particular, the scheme is universally applicable without restriction to a specific unwarping method, and uses no external reference. The deformation artifacts are effectively reduced in the resulting histology volumes, while the natural curvature of the anatomy is preserved. The validity of our method is shown on synthetic data, simulated histology data using a CT data set and real histology data. In the case of the simulated histology where the ground truth was known, the mean Target Registration Error (TRE) between the unwarped and original volume could be reduced to less than 1 pixel on average after six iterations of our proposed method.



FAU Authors / FAU Editors

Daum, Volker Dr.
Graduiertenzentrum der FAU
Lehrstuhl für Informatik 5 (Mustererkennung)
Gaffling, Simone
Hornegger, Joachim Prof. Dr.-Ing.
Lehrstuhl für Informatik 5 (Mustererkennung)
Köstler, Harald PD Dr.-Ing.
Lehrstuhl für Informatik 10 (Systemsimulation)
Maier, Andreas Prof. Dr.-Ing.
Lehrstuhl für Informatik 5 (Mustererkennung)
Steidl, Stefan PD Dr.-Ing.
Lehrstuhl für Informatik 5 (Mustererkennung)


How to cite

APA:
Gaffling, S., Daum, V., Steidl, S., Maier, A., Köstler, H., & Hornegger, J. (2015). A Gauss-Seidel Iteration Scheme for Reference-Free 3-D Histological Image Reconstruction. IEEE Transactions on Medical Imaging, 34/2015(2), 1-17. https://dx.doi.org/10.1109/TMI.2014.2361784

MLA:
Gaffling, Simone, et al. "A Gauss-Seidel Iteration Scheme for Reference-Free 3-D Histological Image Reconstruction." IEEE Transactions on Medical Imaging 34/2015.2 (2015): 1-17.

BibTeX: 

Last updated on 2018-19-04 at 02:44