Combined segmentation and tracking of neural stem-cells

Authors:
K. Althoff;J. Degerman;T. Gustavsson
Affiliations:
Department of Signals and Systems, Chalmers University of Technology, Gothenburg, Sweden;Department of Signals and Systems, Chalmers University of Technology, Gothenburg, Sweden;Department of Signals and Systems, Chalmers University of Technology, Gothenburg, Sweden
Venue:
SCIA'05 Proceedings of the 14th Scandinavian conference on Image Analysis
Year:
2005

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Abstract

In this paper we analyze neural stem/progenitor cells in an time-lapse image sequence. By using information about the previous positions of the cells, we are able to make a better selection of possible cells out of a collection of blob-like objects. As a blob detector we use Laplacian of Gaussian (LoG) filters at multiple scales, and the cell contours of the selected cells are segmented using dynamic programming. After the segmentation process the cells are tracked in the sequence using a combined nearest-neighbor and correlation matching technique. An evaluation of the system show that 95% of the cells were correctly segmented and tracked between consecutive frames.