A computational 3D model for reconstruction of neural stem cells in bright-field time-lapse microscopy

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 microscopy modality 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.