Segmenting and tracking multiple dividing targets using ilastik
Tracking crowded cells or other targets in biology is often a challenging task due to poor signal-to-noise ratio, mutual occlusion, large displacements, little discernibility, and the ability of cells to divide. We here present an open source implementation of conservation tracking (Schiegg et al.,...
Gespeichert in:
| Hauptverfasser: | , , , , , |
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| Dokumenttyp: | Kapitel/Artikel |
| Sprache: | Englisch |
| Veröffentlicht: |
21 May 2016
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| In: |
Focus on bio-image informatics
Year: 2016, Pages: 219-229 |
| DOI: | 10.1007/978-3-319-28549-8_8 |
| Online-Zugang: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1007/978-3-319-28549-8_8
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| Verfasserangaben: | Carsten Haubold, Martin Schiegg, Anna Kreshuk, Stuart Berg, Ullrich Koethe, Fred A. Hamprecht |
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| 520 | |a Tracking crowded cells or other targets in biology is often a challenging task due to poor signal-to-noise ratio, mutual occlusion, large displacements, little discernibility, and the ability of cells to divide. We here present an open source implementation of conservation tracking (Schiegg et al., IEEE international conference on computer vision (ICCV). IEEE, New York, pp 2928-2935, 2013) in the ilastik software framework. This robust tracking-by-assignment algorithm explicitly makes allowance for false positive detections, undersegmentation, and cell division. We give an overview over the underlying algorithm and parameters, and explain the use for a light sheet microscopy sequence of a Drosophila embryo. Equipped with this knowledge, users will be able to track targets of interest in their own data. | ||
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