A1 Refereed original research article in a scientific journal
TrackMate 7: integrating state-of-the-art segmentation algorithms into tracking pipelines
Authors: Ershov Dmitry, Phan Minh-Son, Pylvänäinen Joanna W., Rigaud Stephane U., Le Blanc Laure, Charles-Orszag Arthur, Conway James R.W., Laine Romain F., Roy Nathan H., Bonazzi Daria, Dumenil Guillaume, Jacquemet Guillaume, Tinevez Jean-Yves
Publisher: Nature
Publication year: 2022
Journal: Nature Methods
Journal name in source: NATURE METHODS
Journal acronym: NAT METHODS
Volume: 19
First page : 829
Last page: 832
Number of pages: 12
ISSN: 1548-7091
eISSN: 1548-7105
DOI: https://doi.org/10.1038/s41592-022-01507-1(external)
Web address : https://www.nature.com/articles/s41592-022-01507-1(external)
Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/175931238(external)
TrackMate is an automated tracking software used to analyze bioimages and is distributed as a Fiji plugin. Here, we introduce a new version of TrackMate. TrackMate 7 is built to address the broad spectrum of modern challenges researchers face by integrating state-of-the-art segmentation algorithms into tracking pipelines. We illustrate qualitatively and quantitatively that these new capabilities function effectively across a wide range of bio-imaging experiments.TrackMate 7 combines the benefits of machine and deep learning-based image segmentation with accurate object tracking to enable improved 2D and 3D tracking of diverse objects in biological research.
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