The Cell Tracking Challenge: 10 years of objective benchmarking

J 2023

The Cell Tracking Challenge: 10 years of objective benchmarking

MAŠKA, Martin, Vladimír ULMAN, Pablo DELGADO-RODRIGUEZ, Estibaliz GÓMEZ-DE-MARISCAL, Tereza NEČASOVÁ et. al.

Basic information

Original name

The Cell Tracking Challenge: 10 years of objective benchmarking

Authors

MAŠKA, Martin (203 Czech Republic, guarantor, belonging to the institution), Vladimír ULMAN (203 Czech Republic, belonging to the institution), Pablo DELGADO-RODRIGUEZ (724 Spain), Estibaliz GÓMEZ-DE-MARISCAL (724 Spain), Tereza NEČASOVÁ (203 Czech Republic, belonging to the institution), Fidel A Guerrero PEÑA (76 Brazil), Tsang Ing REN (840 United States of America), Elliot M MEYEROWITZ (840 United States of America), Tim SCHERR (276 Germany), Katharina LÖFFLER (276 Germany), Ralf MIKUT (276 Germany), Tianqi GUO (840 United States of America), Yin WANG (840 United States of America), Jan P ALLEBACH (840 United States of America), Rina BAO (840 United States of America), Noor M AL-SHAKARJI (840 United States of America), Gani RAHMON (840 United States of America), Imad Eddine TOUBAL (840 United States of America), Kannappan PALANIAPPAN (840 United States of America), Filip LUX (203 Czech Republic, belonging to the institution), Petr MATULA (203 Czech Republic, belonging to the institution), Ko SUGAWARA (250 France), Klas E G MAGNUSSON (752 Sweden), Layton AHO (840 United States of America), Andrew R COHEN (840 United States of America), Assaf ARBELLE (376 Israel), Tal BEN-HAIM (376 Israel), Tammy Riklin RAVIV (376 Israel), Fabian ISENSEE (276 Germany), Paul F JÄGER (276 Germany), Klaus H MAIER-HEIN (276 Germany), Yanming ZHU (36 Australia), Cristina EDERRA (724 Spain), Ainhoa URBIOLA (724 Spain), Erik MEIJERING (36 Australia), Alexandre CUNHA (840 United States of America), Arrate MUÑOZ-BARRUTIA (724 Spain), Michal KOZUBEK (203 Czech Republic, belonging to the institution) and Carlos ORTIZ-DE-SOLÓRZANO (724 Spain)

Edition

Nature Methods, 2023, 1548-7091

Other information

Language

English

Type of outcome

Článek v odborném periodiku

Field of Study

10201 Computer sciences, information science, bioinformatics

Country of publisher

United States of America

Confidentiality degree

není předmětem státního či obchodního tajemství

References:

URL

Impact factor

Impact factor: 48.000 in 2022

RIV identification code

RIV/00216224:14330/23:00130820

Organization unit

Faculty of Informatics

DOI

http://dx.doi.org/10.1038/s41592-023-01879-y

UT WoS

000999144000001

Keywords in English

cell segmentation;cell tracking;benchmarking

Abstract

V originále

The Cell Tracking Challenge is an ongoing benchmarking initiative that has become a reference in cell segmentation and tracking algorithm development. Here, we present a significant number of improvements introduced in the challenge since our 2017 report. These include the creation of a new segmentation-only benchmark, the enrichment of the dataset repository with new datasets that increase its diversity and complexity, and the creation of a silver standard reference corpus based on the most competitive results, which will be of particular interest for data-hungry deep learning-based strategies. Furthermore, we present the up-to-date cell segmentation and tracking leaderboards, an in-depth analysis of the relationship between the performance of the state-of-the-art methods and the properties of the datasets and annotations, and two novel, insightful studies about the generalizability and the reusability of top-performing methods. These studies provide critical practical conclusions for both developers and users of traditional and machine learning-based cell segmentation and tracking algorithms.

Links

EF18_046/0016045, research and development project

Name: Modernizace národní infrastruktury pro biologické a medicínské zobrazování Czech-BioImaging

GA21-20374S, research and development project

Name: Segmentace a sledování buněk se složitým tvarem

Investor: Czech Science Foundation

LM2023050, research and development project

Name: Národní infrastruktura pro biologické a medicínské zobrazování

Investor: Ministry of Education, Youth and Sports of the CR, Czech BioImaging: National research infrastructure for biological and medical imaging

MUNI/A/1081/2022, interní kód MU

Name: Modelování, analýza a verifikace (2023)

Investor: Masaryk University

Citovat

MAŠKA, Martin, Vladimír ULMAN, Pablo DELGADO-RODRIGUEZ, Estibaliz GÓMEZ-DE-MARISCAL, Tereza NEČASOVÁ, Fidel A Guerrero PEÑA, Tsang Ing REN, Elliot M MEYEROWITZ, Tim SCHERR, Katharina LÖFFLER, Ralf MIKUT, Tianqi GUO, Yin WANG, Jan P ALLEBACH, Rina BAO, Noor M AL-SHAKARJI, Gani RAHMON, Imad Eddine TOUBAL, Kannappan PALANIAPPAN, Filip LUX, Petr MATULA, Ko SUGAWARA, Klas E G MAGNUSSON, Layton AHO, Andrew R COHEN, Assaf ARBELLE, Tal BEN-HAIM, Tammy Riklin RAVIV, Fabian ISENSEE, Paul F JÄGER, Klaus H MAIER-HEIN, Yanming ZHU, Cristina EDERRA, Ainhoa URBIOLA, Erik MEIJERING, Alexandre CUNHA, Arrate MUÑOZ-BARRUTIA, Michal KOZUBEK and Carlos ORTIZ-DE-SOLÓRZANO. The Cell Tracking Challenge: 10 years of objective benchmarking. Nature Methods. 2023, vol. 20, No 7, p. 1010-1020. ISSN 1548-7091. Available from: https://dx.doi.org/10.1038/s41592-023-01879-y.

@article{2285029,
   author = {Maška, Martin and Ulman, Vladimír and DelgadoandRodriguez, Pablo and GómezanddeandMariscal, Estibaliz and Nečasová, Tereza and Peña, Fidel A Guerrero and Ren, Tsang Ing and Meyerowitz, Elliot M and Scherr, Tim and Löffler, Katharina and Mikut, Ralf and Guo, Tianqi and Wang, Yin and Allebach, Jan P and Bao, Rina and AlandShakarji, Noor M and Rahmon, Gani and Toubal, Imad Eddine and Palaniappan, Kannappan and Lux, Filip and Matula, Petr and Sugawara, Ko and Magnusson, Klas E G and Aho, Layton and Cohen, Andrew R and Arbelle, Assaf and BenandHaim, Tal and Raviv, Tammy Riklin and Isensee, Fabian and Jäger, Paul F and MaierandHein, Klaus H and Zhu, Yanming and Ederra, Cristina and Urbiola, Ainhoa and Meijering, Erik and Cunha, Alexandre and MuñozandBarrutia, Arrate and Kozubek, Michal and OrtizanddeandSolórzano, Carlos},
   article_number = {7},
   doi = {http://dx.doi.org/10.1038/s41592-023-01879-y},
   keywords = {cell segmentation;cell tracking;benchmarking},
   language = {eng},
   issn = {1548-7091},
   journal = {Nature Methods},
   title = {The Cell Tracking Challenge: 10 years of objective benchmarking},
   url = {https://doi.org/10.1038/s41592-023-01879-y},
   volume = {20},
   year = {2023}
}

TY  - JOUR
ID  - 2285029
AU  - Maška, Martin - Ulman, Vladimír - Delgado-Rodriguez, Pablo - Gómez-de-Mariscal, Estibaliz - Nečasová, Tereza - Peña, Fidel A Guerrero - Ren, Tsang Ing - Meyerowitz, Elliot M - Scherr, Tim - Löffler, Katharina - Mikut, Ralf - Guo, Tianqi - Wang, Yin - Allebach, Jan P - Bao, Rina - Al-Shakarji, Noor M - Rahmon, Gani - Toubal, Imad Eddine - Palaniappan, Kannappan - Lux, Filip - Matula, Petr - Sugawara, Ko - Magnusson, Klas E G - Aho, Layton - Cohen, Andrew R - Arbelle, Assaf - Ben-Haim, Tal - Raviv, Tammy Riklin - Isensee, Fabian - Jäger, Paul F - Maier-Hein, Klaus H - Zhu, Yanming - Ederra, Cristina - Urbiola, Ainhoa - Meijering, Erik - Cunha, Alexandre - Muñoz-Barrutia, Arrate - Kozubek, Michal - Ortiz-de-Solórzano, Carlos
PY  - 2023
TI  - The Cell Tracking Challenge: 10 years of objective benchmarking
JF  - Nature Methods
VL  - 20
IS  - 7
SP  - 1010-1020
EP  - 1010-1020
SN  - 15487091
KW  - cell segmentation;cell tracking;benchmarking
UR  - https://doi.org/10.1038/s41592-023-01879-y
N2  - The Cell Tracking Challenge is an ongoing benchmarking initiative that has become a reference in cell segmentation and tracking algorithm development. Here, we present a significant number of improvements introduced in the challenge since our 2017 report. These include the creation of a new segmentation-only benchmark, the enrichment of the dataset repository with new datasets that increase its diversity and complexity, and the creation of a silver standard reference corpus based on the most competitive results, which will be of particular interest for data-hungry deep learning-based strategies. Furthermore, we present the up-to-date cell segmentation and tracking leaderboards, an in-depth analysis of the relationship between the performance of the state-of-the-art methods and the properties of the datasets and annotations, and two novel, insightful studies about the generalizability and the reusability of top-performing methods. These studies provide critical practical conclusions for both developers and users of traditional and machine learning-based cell segmentation and tracking algorithms.
ER  -

MAŠKA, Martin, Vladimír ULMAN, Pablo DELGADO-RODRIGUEZ, Estibaliz GÓMEZ-DE-MARISCAL, Tereza NEČASOVÁ, Fidel A Guerrero PEÑA, Tsang Ing REN, Elliot M MEYEROWITZ, Tim SCHERR, Katharina LÖFFLER, Ralf MIKUT, Tianqi GUO, Yin WANG, Jan P ALLEBACH, Rina BAO, Noor M AL-SHAKARJI, Gani RAHMON, Imad Eddine TOUBAL, Kannappan PALANIAPPAN, Filip LUX, Petr MATULA, Ko SUGAWARA, Klas E G MAGNUSSON, Layton AHO, Andrew R COHEN, Assaf ARBELLE, Tal BEN-HAIM, Tammy Riklin RAVIV, Fabian ISENSEE, Paul F JÄGER, Klaus H MAIER-HEIN, Yanming ZHU, Cristina EDERRA, Ainhoa URBIOLA, Erik MEIJERING, Alexandre CUNHA, Arrate MUÑOZ-BARRUTIA, Michal KOZUBEK and Carlos ORTIZ-DE-SOLÓRZANO. The Cell Tracking Challenge: 10 years of objective benchmarking. \textit{Nature Methods}. 2023, vol.~20, No~7, p.~1010-1020. ISSN~1548-7091. Available from: https://dx.doi.org/10.1038/s41592-023-01879-y.

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