The cell tracking challenge: 10 years of objective benchmarking.

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 segmenta...

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Detalhes bibliográficos
Autores: Maska, M. (Martín)|||/items/25e93789-97c1-4504-bd27-50fb05b7630c, Ulman, V. (Vladimir)|||/items/666613a5-643d-417c-bf2e-6a33b823fbd1, Delgado-Rodríguez, P. (Pablo)|||/items/676b369f-bef2-4983-a3e4-bae3b7d7612c, Gómez-de-Mariscal, E. (Estibaliz)|||/items/12a3032a-c1c8-4190-9e68-da2c4f0c8256, Necasova, T. (Tereza)|||/items/9f96d9fa-4e20-4946-9e1a-c29a4cd9d619, Guerrero-Peña, F.A. (Fidel A.)|||/items/03ae7d07-6672-496f-9d8f-305954bd199f, Ren, T.I. (Tsang Ing)|||/items/220974ae-7f6b-4247-a6aa-81ebdcc3533d, Meyerowitz, E.M. (Elliot M.)|||/items/bd1cd07f-fa84-4675-8f91-b7d442bd51a0, Scherr, T. (Tim)|||/items/307f7010-e907-4bce-9479-5e2dfcde904d, Löffler, K. (Katharina)|||/items/2d96ed3e-4f9f-43fd-828b-dff4f8e26920, Mikut, R. (Ralf)|||/items/719ea3a0-8cfe-46db-85b0-7bbdaec86a23, Guo, T. (Tianqi)|||/items/bd46deab-90a8-419d-a5ad-ba68e3780201, Wang, Y. (Yin)|||/items/207351d1-ff1e-497f-ae99-8e72d6634f5d, Allebach, J.P. (Jan P.)|||/items/1f537ccc-1a4a-4525-aea1-6c9a36e7a2c0, Bao, R. (Rina)|||/items/6dd99e35-461a-4c2d-b60c-94a8f2c811ba, Al-Shakarji, N.M. (Noor M.)|||/items/77ff0680-090c-48b7-ad5c-2870d9b656ca, Rahmon, G. (Gani)|||/items/0884ed4a-3096-4ab9-9f77-089fff2ee10b, Toubal, I.E. (Imad Eddine)|||/items/fdb67641-e7a9-46e4-bdf8-0aa0bd4ba735, Palaniappan, K. (Kannappan)|||/items/b2c0259b-ccd5-48c9-9923-3364be802d3a, Lux, P. (Pilip)|||/items/9f2a811f-e0d7-4752-b194-145180ff2339, Matula, P. (Petr)|||/items/dd226821-b5bf-489e-815a-8b0b8dc4e3e8, Sugawara, G. (Go)|||/items/539e70b9-17f6-402e-9aea-045d95bae604, Magnusson, K.E.G. (Klas E.G.)|||/items/4b88ffdf-3f62-46a9-845e-cd901d3f3733, Aho, L. (Layton)|||/items/49e206ff-1ffc-4372-94b0-6d86fc677b45, Cohen, A.R. (Andrew R.)|||/items/5097bc31-c112-4cb5-bcf2-56638dca4126, Arbelle, A. (Assaf)|||/items/1e93ff80-1f1a-4f01-bddd-10251252366a, Ben-Haim, T. (Tal)|||/items/3aa167fe-1d2b-4be1-b62a-585aa01f89d8, Raviv, T.R. (Tammy Riklin)|||/items/965d6703-7dce-4746-8604-3f159cb74137, Issensee, F. (Fabian)|||/items/78211bf8-f2c4-457f-b315-4cd1e629a59a, Jäger, P.F.(Paul F.)|||/items/b57dcc84-1a92-43c9-991a-32a28036fa00, Maier-Hein, K.H. (Klaus H.)|||/items/d00486c4-d6ab-477b-ba9d-1caccc6225a6, Zhu, Y. (Yanming)|||/items/76bbbaab-5f2a-43bf-a962-a4077f014d47, Ederra, C. (Cristina)|||/items/ac401bff-cc77-44f1-b7f1-ffdeffed99fa, Urbiola, A. (Ainhoa)|||/items/3947b776-50ee-41c9-b028-b8ba0bfa0362, Meijering, E. (Erik)|||/items/bc616a91-c742-4df2-9d57-96fb21acd4e6, Cunha, A. (Alexandre)|||/items/6c90d63d-f1c3-476f-930a-58bc61cd37f0, Muñoz-Barrutia, A. (Arrate)|||/items/38d480d3-2ba0-4f49-a7e3-c3c940d927ba, Kozubek, M. (Michal)|||/items/fed8146f-b2ba-4bcb-916c-69cf8c161f46, Ortiz-de-Solorzano, C. (Carlos)|||/items/b2c0bd13-68a7-437c-b67f-2e5f1a9f0324
Tipo de documento: artigo
Data de publicação:2023
País:España
Recursos:Universidad de Navarra
Repositório:Dadun. Depósito Académico Digital de la Universidad de Navarra
Idioma:inglês
OAI Identifier:oai:dadun.unav.edu:10171/111295
Acesso em linha:https://hdl.handle.net/10171/111295
Access Level:Acceso aberto
Palavra-chave:Cell tracking
Deep learning algorithms
Cell segmentation
Technical performance
Descrição
Resumo: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.

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