A graph-based cell tracking algorithm with few manually tunable parameters and automated segmentation error correction.

Automatic cell segmentation and tracking enables to gain quantitative insights into the processes driving cell migration. To investigate new data with minimal manual effort, cell tracking algorithms should be easy to apply and reduce manual curation time by providing automatic correction of segmenta...

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Autores principales:	Katharina Löffler, Tim Scherr, Ralf Mikut
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/c2468105f9d34cb293f7a8ada0e5f24e
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id	oai:doaj.org-article:c2468105f9d34cb293f7a8ada0e5f24e
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spelling	oai:doaj.org-article:c2468105f9d34cb293f7a8ada0e5f24e2021-12-02T20:08:30ZA graph-based cell tracking algorithm with few manually tunable parameters and automated segmentation error correction.1932-620310.1371/journal.pone.0249257https://doaj.org/article/c2468105f9d34cb293f7a8ada0e5f24e2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0249257https://doaj.org/toc/1932-6203Automatic cell segmentation and tracking enables to gain quantitative insights into the processes driving cell migration. To investigate new data with minimal manual effort, cell tracking algorithms should be easy to apply and reduce manual curation time by providing automatic correction of segmentation errors. Current cell tracking algorithms, however, are either easy to apply to new data sets but lack automatic segmentation error correction, or have a vast set of parameters that needs either manual tuning or annotated data for parameter tuning. In this work, we propose a tracking algorithm with only few manually tunable parameters and automatic segmentation error correction. Moreover, no training data is needed. We compare the performance of our approach to three well-performing tracking algorithms from the Cell Tracking Challenge on data sets with simulated, degraded segmentation-including false negatives, over- and under-segmentation errors. Our tracking algorithm can correct false negatives, over- and under-segmentation errors as well as a mixture of the aforementioned segmentation errors. On data sets with under-segmentation errors or a mixture of segmentation errors our approach performs best. Moreover, without requiring additional manual tuning, our approach ranks several times in the top 3 on the 6th edition of the Cell Tracking Challenge.Katharina LöfflerTim ScherrRalf MikutPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 9, p e0249257 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Katharina Löffler Tim Scherr Ralf Mikut A graph-based cell tracking algorithm with few manually tunable parameters and automated segmentation error correction.
description	Automatic cell segmentation and tracking enables to gain quantitative insights into the processes driving cell migration. To investigate new data with minimal manual effort, cell tracking algorithms should be easy to apply and reduce manual curation time by providing automatic correction of segmentation errors. Current cell tracking algorithms, however, are either easy to apply to new data sets but lack automatic segmentation error correction, or have a vast set of parameters that needs either manual tuning or annotated data for parameter tuning. In this work, we propose a tracking algorithm with only few manually tunable parameters and automatic segmentation error correction. Moreover, no training data is needed. We compare the performance of our approach to three well-performing tracking algorithms from the Cell Tracking Challenge on data sets with simulated, degraded segmentation-including false negatives, over- and under-segmentation errors. Our tracking algorithm can correct false negatives, over- and under-segmentation errors as well as a mixture of the aforementioned segmentation errors. On data sets with under-segmentation errors or a mixture of segmentation errors our approach performs best. Moreover, without requiring additional manual tuning, our approach ranks several times in the top 3 on the 6th edition of the Cell Tracking Challenge.
format	article
author	Katharina Löffler Tim Scherr Ralf Mikut
author_facet	Katharina Löffler Tim Scherr Ralf Mikut
author_sort	Katharina Löffler
title	A graph-based cell tracking algorithm with few manually tunable parameters and automated segmentation error correction.
title_short	A graph-based cell tracking algorithm with few manually tunable parameters and automated segmentation error correction.
title_full	A graph-based cell tracking algorithm with few manually tunable parameters and automated segmentation error correction.
title_fullStr	A graph-based cell tracking algorithm with few manually tunable parameters and automated segmentation error correction.
title_full_unstemmed	A graph-based cell tracking algorithm with few manually tunable parameters and automated segmentation error correction.
title_sort	graph-based cell tracking algorithm with few manually tunable parameters and automated segmentation error correction.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/c2468105f9d34cb293f7a8ada0e5f24e
work_keys_str_mv	AT katharinaloffler agraphbasedcelltrackingalgorithmwithfewmanuallytunableparametersandautomatedsegmentationerrorcorrection AT timscherr agraphbasedcelltrackingalgorithmwithfewmanuallytunableparametersandautomatedsegmentationerrorcorrection AT ralfmikut agraphbasedcelltrackingalgorithmwithfewmanuallytunableparametersandautomatedsegmentationerrorcorrection AT katharinaloffler graphbasedcelltrackingalgorithmwithfewmanuallytunableparametersandautomatedsegmentationerrorcorrection AT timscherr graphbasedcelltrackingalgorithmwithfewmanuallytunableparametersandautomatedsegmentationerrorcorrection AT ralfmikut graphbasedcelltrackingalgorithmwithfewmanuallytunableparametersandautomatedsegmentationerrorcorrection
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A graph-based cell tracking algorithm with few manually tunable parameters and automated segmentation error correction.

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