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.
Proc. SPIE. 5747, Medical Imaging 2005: Image Processing
KEYWORDS: Image processing algorithms and systems, Detection and tracking algorithms, Image segmentation, Computer programming, Medical imaging, Stem cells, Neurons, Time lapse microscopy, Neurogenesis, Automatic tracking
This paper describes an algorithm for tracking neural stem/progenitor cells in a time-lapse microscopy image sequence. The cells were segmented in a semiautomatic way using dynamic programming. Since the interesting cells were identified by fluorescent staining at the end of the sequence, the tracking was performed backwards. The number of detected cells varied throughout the sequence: cells could appear or disappear at the image boundaries or at cell clusters, some cells split, and the segmentation was not always correct. To solve this asymmetric assignment problem, a modified version of the auction algorithm by Bertsekas was used. The assignment weights were calculated based on distance, correlation and size between possible matching cells. Cell splits are of special interest, therefore tracks without a matching cell were divided into two groups: 1. Merging cells (splitting cells, moving forward in time) and 2. Non-merging cells. These groups were separated based on difference in size of the involved cells, and difference in image intensity of the contour and interior of the possibly merged cell. The tracking algorithm was evaluated using a sequence consisting of 57 images, each image containing approximately 50 cells. The evaluation showed that 99% of the cell-to-cell associations were correct. In most cases, only one association per track was incorrect so in total 55 out of 78 different tracks in the sequence were tracked correctly. Further improvements will be to apply interleaved segmentation and tracking to produce a more reliable segmentation as well as better tracking results.
This paper presents hardware and software procedures for automated cell tracking and migration modeling. A time-lapse microscopy system equipped with a computer controllable motorized stage was developed. The performance of this stage was improved by incorporating software algorithms for stage motion displacement compensation and auto focus. The microscope is suitable for in-vitro stem cell studies and allows for multiple cell culture image sequence acquisition. This enables comparative studies concerning rate of cell splits, average cell motion velocity, cell motion as a function of cell sample density and many more. Several cell segmentation procedures are described as well as a cell tracking algorithm. Statistical methods for describing cell migration patterns are presented. In particular, the Hidden Markov Model (HMM) was investigated. Results indicate that if the cell motion can be described as a non-stationary stochastic process, then the HMM can adequately model aspects of its dynamic behavior.