Detecting cellular events in brain cortex using time-lapse microscopy

We present a framework for cell tracking in a highly cluttered environment in live cell imaging from mouse brain cortex. Our goal is to track cells over a long period of time for intracellular calciumion concentration in order to detect important cellular events such as neural activity and cell division. Since traditional object tracking approach such as segmentation followed by tracking is not applicable due to the cluttered environment, we developed a pipeline featuring a combined cell tracking and segmentation scheme using particle filtering and level set based active contour methods. Our results show that we can track multiple cells with 90% of results meeting the error tolerance.