A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä
TrackMate 7: integrating state-of-the-art segmentation algorithms into tracking pipelines
Tekijät: 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
Kustantaja: Nature
Julkaisuvuosi: 2022
Journal: Nature Methods
Tietokannassa oleva lehden nimi: NATURE METHODS
Lehden akronyymi: NAT METHODS
Vuosikerta: 19
Aloitussivu: 829
Lopetussivu: 832
Sivujen määrä: 12
ISSN: 1548-7091
eISSN: 1548-7105
DOI: https://doi.org/10.1038/s41592-022-01507-1
Verkko-osoite: https://www.nature.com/articles/s41592-022-01507-1
Rinnakkaistallenteen osoite: https://research.utu.fi/converis/portal/detail/Publication/175931238
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.
Ladattava julkaisu This is an electronic reprint of the original article. |  |
