A diffusion model for detecting and classifying vesicle fusion and undocking events

Lorenz Berger, Majid Mirmehdi, Sam Reed, Jeremy Tavaré

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

1 Citation (Scopus)

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.
Original languageEnglish
Title of host publicationMedical Image Computing and Computer-Assisted Intervention – MICCAI 2012
Subtitle of host publication15th International Conference, Nice, France, October 1-5, 2012, Proceedings, Part III
PublisherSpringer
Pages329-336
Number of pages8
Volume3
ISBN (Electronic)978-3-642-33454-2
ISBN (Print)978-3-642-33453-5
DOIs
Publication statusPublished - 1 Oct 2012

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume7512

Keywords

  • Sensitivity and Specificity
  • Microscopy, Fluorescence
  • Image Interpretation, Computer-Assisted
  • Computer Simulation
  • Reproducibility of Results
  • Membrane Fusion
  • Cell Tracking
  • Cell Membrane
  • Image Enhancement
  • Diffusion
  • Transport Vesicles
  • Models, Biological

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