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author Ulman, Vladimír
Maška, Martin
Magnusson, Klas E G
Ronneberger, Olaf
Haubold, Carsten
Harder, Nathalie
Matula, Pavel
Matula, Petr
Svoboda, David
Radojevic, Miroslav
Smal, Ihor
Rohr, Karl
Jaldén, Joakim
Blau, Helen M
Dzyubachyk, Oleh
Lelieveldt, Boudewijn
Xiao, Pengdong
Li, Yuexiang
Cho, Siu-Yeung
Dufour, Alexandre C
Olivo-Marin, Jean-Christophe
Reyes-Aldasoro, Constantino C
Solis-Lemus, Jose A
Bensch, Robert
Brox, Thomas
Stegmaier, Johannes
Mikut, Ralf
Wolf, Steffen
Hamprecht, Fred A
Esteves, Tiago
Quelhas, Pedro
Demirel, Ömer
Malmström, Lars
Jug, Florian
Tomancak, Pavel
Meijering, Erik
Muñoz-Barrutia, Arrate
Kozubek, Michal
Ortiz-de-Solorzano, Carlos
author_facet Ulman, Vladimír
Maška, Martin
Magnusson, Klas E G
Ronneberger, Olaf
Haubold, Carsten
Harder, Nathalie
Matula, Pavel
Matula, Petr
Svoboda, David
Radojevic, Miroslav
Smal, Ihor
Rohr, Karl
Jaldén, Joakim
Blau, Helen M
Dzyubachyk, Oleh
Lelieveldt, Boudewijn
Xiao, Pengdong
Li, Yuexiang
Cho, Siu-Yeung
Dufour, Alexandre C
Olivo-Marin, Jean-Christophe
Reyes-Aldasoro, Constantino C
Solis-Lemus, Jose A
Bensch, Robert
Brox, Thomas
Stegmaier, Johannes
Mikut, Ralf
Wolf, Steffen
Hamprecht, Fred A
Esteves, Tiago
Quelhas, Pedro
Demirel, Ömer
Malmström, Lars
Jug, Florian
Tomancak, Pavel
Meijering, Erik
Muñoz-Barrutia, Arrate
Kozubek, Michal
Ortiz-de-Solorzano, Carlos
author_sort Ulman, Vladimír
building Nottingham Research Data Repository
collection Online Access
description We present a combined report on the results of three editions of the Cell Tracking Challenge, an ongoing initiative aimed at promoting the development and objective evaluation of cell segmentation and tracking algorithms. With 21 participating algorithms and a data repository consisting of 13 data sets from various microscopy modalities, the challenge displays today's state-of-the-art methodology in the field. We analyzed the challenge results using performance measures for segmentation and tracking that rank all participating methods. We also analyzed the performance of all of the algorithms in terms of biological measures and practical usability. Although some methods scored high in all technical aspects, none obtained fully correct solutions. We found that methods that either take prior information into account using learning strategies or analyze cells in a global spatiotemporal video context performed better than other methods under the segmentation and tracking scenarios included in the challenge.
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institution University of Nottingham Malaysia Campus
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language English
last_indexed 2025-11-14T20:27:22Z
publishDate 2017
publisher Springer Nature
recordtype eprints
repository_type Digital Repository
spelling nottingham-532872018-08-10T06:44:38Z https://eprints.nottingham.ac.uk/53287/ An objective comparison of cell-tracking algorithms Ulman, Vladimír Maška, Martin Magnusson, Klas E G Ronneberger, Olaf Haubold, Carsten Harder, Nathalie Matula, Pavel Matula, Petr Svoboda, David Radojevic, Miroslav Smal, Ihor Rohr, Karl Jaldén, Joakim Blau, Helen M Dzyubachyk, Oleh Lelieveldt, Boudewijn Xiao, Pengdong Li, Yuexiang Cho, Siu-Yeung Dufour, Alexandre C Olivo-Marin, Jean-Christophe Reyes-Aldasoro, Constantino C Solis-Lemus, Jose A Bensch, Robert Brox, Thomas Stegmaier, Johannes Mikut, Ralf Wolf, Steffen Hamprecht, Fred A Esteves, Tiago Quelhas, Pedro Demirel, Ömer Malmström, Lars Jug, Florian Tomancak, Pavel Meijering, Erik Muñoz-Barrutia, Arrate Kozubek, Michal Ortiz-de-Solorzano, Carlos We present a combined report on the results of three editions of the Cell Tracking Challenge, an ongoing initiative aimed at promoting the development and objective evaluation of cell segmentation and tracking algorithms. With 21 participating algorithms and a data repository consisting of 13 data sets from various microscopy modalities, the challenge displays today's state-of-the-art methodology in the field. We analyzed the challenge results using performance measures for segmentation and tracking that rank all participating methods. We also analyzed the performance of all of the algorithms in terms of biological measures and practical usability. Although some methods scored high in all technical aspects, none obtained fully correct solutions. We found that methods that either take prior information into account using learning strategies or analyze cells in a global spatiotemporal video context performed better than other methods under the segmentation and tracking scenarios included in the challenge. Springer Nature 2017-10-30 Article PeerReviewed application/pdf en https://eprints.nottingham.ac.uk/53287/1/CTC_Final.pdf Ulman, Vladimír, Maška, Martin, Magnusson, Klas E G, Ronneberger, Olaf, Haubold, Carsten, Harder, Nathalie, Matula, Pavel, Matula, Petr, Svoboda, David, Radojevic, Miroslav, Smal, Ihor, Rohr, Karl, Jaldén, Joakim, Blau, Helen M, Dzyubachyk, Oleh, Lelieveldt, Boudewijn, Xiao, Pengdong, Li, Yuexiang, Cho, Siu-Yeung, Dufour, Alexandre C, Olivo-Marin, Jean-Christophe, Reyes-Aldasoro, Constantino C, Solis-Lemus, Jose A, Bensch, Robert, Brox, Thomas, Stegmaier, Johannes, Mikut, Ralf, Wolf, Steffen, Hamprecht, Fred A, Esteves, Tiago, Quelhas, Pedro, Demirel, Ömer, Malmström, Lars, Jug, Florian, Tomancak, Pavel, Meijering, Erik, Muñoz-Barrutia, Arrate, Kozubek, Michal and Ortiz-de-Solorzano, Carlos (2017) An objective comparison of cell-tracking algorithms. Nature Methods, 14 (12). pp. 1141-1152. ISSN 1548-7091 Cell tracking; cell segmentation; Tracking algorithms http://dx.doi.org/10.1038/nmeth.4473 doi:10.1038/nmeth.4473 doi:10.1038/nmeth.4473
spellingShingle Cell tracking; cell segmentation; Tracking algorithms
Ulman, Vladimír
Maška, Martin
Magnusson, Klas E G
Ronneberger, Olaf
Haubold, Carsten
Harder, Nathalie
Matula, Pavel
Matula, Petr
Svoboda, David
Radojevic, Miroslav
Smal, Ihor
Rohr, Karl
Jaldén, Joakim
Blau, Helen M
Dzyubachyk, Oleh
Lelieveldt, Boudewijn
Xiao, Pengdong
Li, Yuexiang
Cho, Siu-Yeung
Dufour, Alexandre C
Olivo-Marin, Jean-Christophe
Reyes-Aldasoro, Constantino C
Solis-Lemus, Jose A
Bensch, Robert
Brox, Thomas
Stegmaier, Johannes
Mikut, Ralf
Wolf, Steffen
Hamprecht, Fred A
Esteves, Tiago
Quelhas, Pedro
Demirel, Ömer
Malmström, Lars
Jug, Florian
Tomancak, Pavel
Meijering, Erik
Muñoz-Barrutia, Arrate
Kozubek, Michal
Ortiz-de-Solorzano, Carlos
An objective comparison of cell-tracking algorithms
title An objective comparison of cell-tracking algorithms
title_full An objective comparison of cell-tracking algorithms
title_fullStr An objective comparison of cell-tracking algorithms
title_full_unstemmed An objective comparison of cell-tracking algorithms
title_short An objective comparison of cell-tracking algorithms
title_sort objective comparison of cell-tracking algorithms
topic Cell tracking; cell segmentation; Tracking algorithms
url https://eprints.nottingham.ac.uk/53287/
https://eprints.nottingham.ac.uk/53287/
https://eprints.nottingham.ac.uk/53287/