Abstract
Fluorescently-tagged proteins located on vesicles can fuse with the surface membrane (visualised as a ‘puff’) or undock and return back into the bulk of the cell. Detection and quantitative measurement of these events from time-lapse videos has proven difficult. We propose a novel approach to detect fusion and undocking events by first searching for docked vesicles that ‘disappear’ from the field of view, and then using a diffusion model to classify them as either fusion or undocking events. We can also use the same searching method to identify docking events. We present comparative results against existing algorithms.
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Berger, L., Mirmehdi, M., Reed, S., Tavaré, J. (2012). A Diffusion Model for Detecting and Classifying Vesicle Fusion and Undocking Events. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012. MICCAI 2012. Lecture Notes in Computer Science, vol 7512. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33454-2_41
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DOI: https://doi.org/10.1007/978-3-642-33454-2_41
Publisher Name: Springer, Berlin, Heidelberg
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