International Conference on Medical Image Computing and Computer-Assisted Intervention

MICCAI 2014: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014 pp 154-161

Event Detection by Feature Unpredictability in Phase-Contrast Videos of Cell Cultures

  • Melih Kandemir
  • Jose C. Rubio
  • Ute Schmidt
  • Christian Wojek
  • Johannes Welbl
  • Björn Ommer
  • Fred A. Hamprecht
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8674)

Abstract

In this work we propose a novel framework for generic event monitoring in live cell culture videos, built on the assumption that unpredictable observations should correspond to biological events. We use a small set of event-free data to train a multioutput multikernel Gaussian process model that operates as an event predictor by performing autoregression on a bank of heterogeneous features extracted from consecutive frames of a video sequence. We show that the prediction error of this model can be used as a probability measure of the presence of relevant events, that can enable users to perform further analysis or monitoring of large-scale non-annotated data. We validate our approach in two phase-contrast sequence data sets containing mitosis and apoptosis events: a new private dataset of human bone cancer (osteosarcoma) cells and a benchmark dataset of stem cells.

Keywords

Event detection mitosis apoptosis cell cultures phase-contrast imaging 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Melih Kandemir
    • 1
  • Jose C. Rubio
    • 1
  • Ute Schmidt
    • 2
  • Christian Wojek
    • 3
  • Johannes Welbl
    • 1
  • Björn Ommer
    • 1
  • Fred A. Hamprecht
    • 1
  1. 1.Heidelberg University HCI/IWRHeidelbergGermany
  2. 2.Carl Zeiss Microscopy GmbHJenaGermany
  3. 3.Carl Zeiss AGOberkochenGermany

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