Abstract
Tracking cells is one of the main challenges in biology, as it often requires time-consuming annotations and the images can have a low signal-to-noise ratio while containing a large number of cells. Here we present two methods for detecting and tracking cells using the open-source Fiji and ilastik frameworks. A straightforward approach is described using Fiji, consisting of a pre-processing and segmentation phase followed by a tracking phase, based on the overlapping of objects along the image sequence. Using ilastik, a classifier is trained through manual annotations to both detect cells over the background and be able to recognize false detections and merging cells. We describe these two methods in a step-by-step fashion, using as example a time-lapse microscopy movie of HeLa cells.
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Acknowledgments
This work was financed by a fellowship (LCF/BQ/IN17/11620069) from “la Caixa” Foundation (ID 100010434) and by the Spanish Ministry of Economy and Competitiveness grant MDM-1025-0502 through the Maria de Maeztu Units of Excellence in R&D program. We also acknowledge support from the European Commission through the NEUBIAS network (COST action no. CA15124).
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Urru, A., González Ballester, M.A., Zhang, C. (2019). 2D + Time Object Tracking Using Fiji and ilastik. In: Rebollo, E., Bosch, M. (eds) Computer Optimized Microscopy. Methods in Molecular Biology, vol 2040. Humana, New York, NY. https://doi.org/10.1007/978-1-4939-9686-5_20
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DOI: https://doi.org/10.1007/978-1-4939-9686-5_20
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