Bringing TrackMate in the era of machine-learning and deep-learning.

Ershov, D.; Phan, M.-S.; Pylvänäinen, J. W.; Rigaud, S. U.; Le Blanc, L.; Charles-Orszag, A.; Conway, J. R. W.; Laine, R. F.; Roy, N. H.; Bonazzi, D.; Dumenil, G.; Jacquemet, G.; Tinevez, J.-Y.

doi:10.1101/2021.09.03.458852

bioRxiv · 10.1101/2021.09.03.458852

Bringing TrackMate in the era of machine-learning and deep-learning.

Ershov, D.·Phan, M.-S.·Pylvänäinen, J. W.·Rigaud, S. U.·Le Blanc, L.·Charles-Orszag, A.·Conway, J. R. W.·Laine, R. F.·Roy, N. H.·Bonazzi, D.·Dumenil, G.·Jacquemet, G.·Tinevez, J.-Y.

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Abstract

TrackMate is an automated tracking software used to analyze bioimages and distributed as a Fiji plugin. Here we introduce a new version of TrackMate rewritten to improve performance and usability, and integrating several popular machine and deep learning algorithms to improve versatility. We illustrate how these new components can be used to efficiently track objects from brightfield and fluorescence microscopy images across a wide range of bio-imaging experiments.

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Ershov, D., Phan, M.-S., Pylvänäinen, J. W., Rigaud, S. U., Le Blanc, L., Charles-Orszag, A., Conway, J. R. W., Laine, R. F., Roy, N. H., Bonazzi, D., Dumenil, G., Jacquemet, G., Tinevez, J.-Y.. 2021-09-03. Bringing TrackMate in the era of machine-learning and deep-learning.. https://doi.org/10.1101/2021.09.03.458852

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