Segmentation and tracking of neural stem cell

Abstract

In order to understand the development of stem cells into specialized mature cells it is necessary to study the growth of cells in culture. For this purpose it is very useful to have an efficient computerized cell tracking system. In order to get reliable tracking results it is important to have good and robust segmentation of the cells. To achieve this we have implemented three levels of segmentation: based on fuzzy threshold and watershed segmentation of a fuzzy gray weighted distance transformed image; based on a fast geometric active contour model by the level set algorithm and interactively inspected and corrected on the crucial first frame. For the tracking all cells are classified into inactive, active, dividing and clustered cells. A special backtracking step is used to automatically correct for some common errors that appear in the initial forward tracking process. © Springer-Verlag Berlin Heidelberg 2005.