Efficient Detection and Tracking of Multiple Vesicles in Video Microscopy

We describe an algorithm for the efficient triage of events of interest in video microscopy. The specific application involves the detection and tracking of multiple possibly overlapping vesicles in total internal reflection fluorescent microscopy images. A statistical model for the dynamic image data of multiple vesicles allows us to properly weight various hypotheses online. The goal is to find the most likely trajectories given a sequence of images. The computational challenge is addressed by defining a sequence of coarse-to-fine tests, derived from the statistical model, to quickly eliminate most candidate positions at each time frame. The computational load of the tests is initially very low and gradually increases as the false positives become more difficult to eliminate. Only at the last step, parameters are estimated from a complete time-dependent model. Processing time thus mainly depends on the number of vesicles in the image and not on image size.