Name
Title | ||
|---|---|---|
Event detection by feature unpredictability in phase-contrast videos of cell cultures. |
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. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/978-3-319-10470-6_20 | Lecture Notes in Computer Science |
Keywords | Field | DocType |
Event detection,mitosis,apoptosis,cell cultures,phase-contrast imaging | Event monitoring,Phase contrast microscopy,Autoregressive model,Computer vision,Mean squared prediction error,Pattern recognition,Computer science,Probability measure,Multikernel,Gaussian process,Artificial intelligence,Small set | Conference |
Volume | Issue | ISSN |
8674 | Pt 2 | 0302-9743 |
Citations | PageRank | References |
2 | 0.40 | 9 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Melih Kandemir | 1 | 182 | 16.91 |
| Jose C Rubio | 2 | 8 | 0.83 |
| Ute Schmidt | 3 | 2 | 0.40 |
| Christian Wojek | 4 | 4 | 0.77 |
| Johannes Welbl | 5 | 147 | 7.25 |
| Björn Ommer | 6 | 2 | 0.40 |
| Fred A. Hamprecht | 7 | 962 | 76.24 |