
Proceedings Paper
A computational 3D model for reconstruction of neural stem cells in bright-field time-lapse microscopyFormat | Member Price | Non-Member Price |
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Paper Abstract
This paper describes a computational model for image formation of in-vitro adult hippocampal progenitor (AHP)
cells, in bright-field time-lapse microscopy. Although this microscopymodality barely generates sufficient contrast
for imaging translucent cells, we show that by using a stack of defocused image slices it is possible to extract
position and shape of spherically shaped specimens, such as the AHP cells. This inverse problem was solved
by modeling the physical objects and image formation system, and using an iterative nonlinear optimization
algorithm to minimize the difference between the reconstructed and measured image stack. By assuming that
the position and shape of the cells do not change significantly between two time instances, we can optimize
these parameters using the previous time instance in a Bayesian estimation approach. The 3D reconstruction
algorithm settings, such as focal sampling distance, and PSF, were calibrated using latex spheres of known size
and refractive index. By using the residual between reconstructed and measured image intensities, we computed
a peak signal-to-noise ratio (PSNR) to 28 dB for the sphere stack. A biological specimen analysis was done using
an AHP cell, where reconstruction PSNR was 28 dB as well. The cell was immuno-histochemically stained and
scanned in a confocal microscope, in order to compare our cell model to a ground truth. After convergence the
modelled cell volume had an error of less than one percent.
Paper Details
Date Published: 28 February 2007
PDF: 10 pages
Proc. SPIE 6498, Computational Imaging V, 64981E (28 February 2007); doi: 10.1117/12.704096
Published in SPIE Proceedings Vol. 6498:
Computational Imaging V
Charles A. Bouman; Eric L. Miller; Ilya Pollak, Editor(s)
PDF: 10 pages
Proc. SPIE 6498, Computational Imaging V, 64981E (28 February 2007); doi: 10.1117/12.704096
Show Author Affiliations
J. Degerman, Chalmers Univ. of Technology (Sweden)
E. Winterfors, Univ. Pierre et Marie Curie (France)
E. Winterfors, Univ. Pierre et Marie Curie (France)
J. Faijerson, Sahlgrenska Academy at Göteborg Univ. (Sweden)
T. Gustavsson, Chalmers Univ. of Technology (Sweden)
T. Gustavsson, Chalmers Univ. of Technology (Sweden)
Published in SPIE Proceedings Vol. 6498:
Computational Imaging V
Charles A. Bouman; Eric L. Miller; Ilya Pollak, Editor(s)
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