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| 001 | 1015298 | ||
| 005 | 20231122201849.0 | ||
| 024 | 7 | _ | |a 10.48550/ARXIV.2308.05864 |2 doi |
| 024 | 7 | _ | |a 10.48550/arXiv.2308.05864 |2 doi |
| 024 | 7 | _ | |a 10.34734/FZJ-2023-03643 |2 datacite_doi |
| 037 | _ | _ | |a FZJ-2023-03643 |
| 041 | _ | _ | |a English |
| 100 | 1 | _ | |a Ma, Jun |b 0 |
| 245 | _ | _ | |a The Multi-modality Cell Segmentation Challenge: Towards Universal Solutions |
| 260 | _ | _ | |c 2023 |b arXiv |
| 336 | 7 | _ | |a Preprint |b preprint |m preprint |0 PUB:(DE-HGF)25 |s 1700655996_31605 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a WORKING_PAPER |2 ORCID |
| 336 | 7 | _ | |a Electronic Article |0 28 |2 EndNote |
| 336 | 7 | _ | |a preprint |2 DRIVER |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
| 336 | 7 | _ | |a Output Types/Working Paper |2 DataCite |
| 520 | _ | _ | |a Cell segmentation is a critical step for quantitative single-cell analysis in microscopy images. Existing cell segmentation methods are often tailored to specific modalities or require manual interventions to specify hyperparameters in different experimental settings. Here, we present a multi-modality cell segmentation benchmark, comprising over 1500 labeled images derived from more than 50 diverse biological experiments. The top participants developed a Transformer-based deep-learning algorithm that not only exceeds existing methods, but can also be applied to diverse microscopy images across imaging platforms and tissue types without manual parameter adjustments. This benchmark and the improved algorithm offer promising avenues for more accurate and versatile cell analysis in microscopy imaging. |
| 536 | _ | _ | |a 5254 - Neuroscientific Data Analytics and AI (POF4-525) |0 G:(DE-HGF)POF4-5254 |c POF4-525 |f POF IV |x 0 |
| 536 | _ | _ | |a DFG project 347572269 - Heterogenität von Zytoarchitektur, Chemoarchitektur und Konnektivität in einem großskaligen Computermodell der menschlichen Großhirnrinde (347572269) |0 G:(GEPRIS)347572269 |c 347572269 |x 1 |
| 536 | _ | _ | |a HBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539) |0 G:(EU-Grant)945539 |c 945539 |f H2020-SGA-FETFLAG-HBP-2019 |x 2 |
| 536 | _ | _ | |a HIBALL - Helmholtz International BigBrain Analytics and Learning Laboratory (HIBALL) (InterLabs-0015) |0 G:(DE-HGF)InterLabs-0015 |c InterLabs-0015 |x 3 |
| 536 | _ | _ | |a Helmholtz AI - Helmholtz Artificial Intelligence Coordination Unit – Local Unit FZJ (E.40401.62) |0 G:(DE-Juel-1)E.40401.62 |c E.40401.62 |x 4 |
| 588 | _ | _ | |a Dataset connected to DataCite |
| 650 | _ | 7 | |a Image and Video Processing (eess.IV) |2 Other |
| 650 | _ | 7 | |a Computer Vision and Pattern Recognition (cs.CV) |2 Other |
| 650 | _ | 7 | |a Machine Learning (cs.LG) |2 Other |
| 650 | _ | 7 | |a Quantitative Methods (q-bio.QM) |2 Other |
| 650 | _ | 7 | |a FOS: Electrical engineering, electronic engineering, information engineering |2 Other |
| 650 | _ | 7 | |a FOS: Computer and information sciences |2 Other |
| 650 | _ | 7 | |a FOS: Biological sciences |2 Other |
| 700 | 1 | _ | |a Xie, Ronald |b 1 |
| 700 | 1 | _ | |a Ayyadhury, Shamini |b 2 |
| 700 | 1 | _ | |a Ge, Cheng |b 3 |
| 700 | 1 | _ | |a Gupta, Anubha |b 4 |
| 700 | 1 | _ | |a Gupta, Ritu |b 5 |
| 700 | 1 | _ | |a Gu, Song |b 6 |
| 700 | 1 | _ | |a Zhang, Yao |b 7 |
| 700 | 1 | _ | |a Lee, Gihun |b 8 |
| 700 | 1 | _ | |a Kim, Joonkee |b 9 |
| 700 | 1 | _ | |a Lou, Wei |b 10 |
| 700 | 1 | _ | |a Li, Haofeng |b 11 |
| 700 | 1 | _ | |a Upschulte, Eric |0 P:(DE-Juel1)177675 |b 12 |u fzj |
| 700 | 1 | _ | |a Dickscheid, Timo |0 P:(DE-Juel1)165746 |b 13 |u fzj |
| 700 | 1 | _ | |a de Almeida, José Guilherme |b 14 |
| 700 | 1 | _ | |a Wang, Yixin |b 15 |
| 700 | 1 | _ | |a Han, Lin |b 16 |
| 700 | 1 | _ | |a Yang, Xin |b 17 |
| 700 | 1 | _ | |a Labagnara, Marco |b 18 |
| 700 | 1 | _ | |a Rahi, Sahand Jamal |b 19 |
| 700 | 1 | _ | |a Kempster, Carly |b 20 |
| 700 | 1 | _ | |a Pollitt, Alice |b 21 |
| 700 | 1 | _ | |a Espinosa, Leon |b 22 |
| 700 | 1 | _ | |a Mignot, Tâm |b 23 |
| 700 | 1 | _ | |a Middeke, Jan Moritz |b 24 |
| 700 | 1 | _ | |a Eckardt, Jan-Niklas |b 25 |
| 700 | 1 | _ | |a Li, Wangkai |b 26 |
| 700 | 1 | _ | |a Li, Zhaoyang |b 27 |
| 700 | 1 | _ | |a Cai, Xiaochen |b 28 |
| 700 | 1 | _ | |a Bai, Bizhe |b 29 |
| 700 | 1 | _ | |a Greenwald, Noah F. |b 30 |
| 700 | 1 | _ | |a Van Valen, David |b 31 |
| 700 | 1 | _ | |a Weisbart, Erin |b 32 |
| 700 | 1 | _ | |a Cimini, Beth A. |b 33 |
| 700 | 1 | _ | |a Li, Zhuoshi |b 34 |
| 700 | 1 | _ | |a Zuo, Chao |b 35 |
| 700 | 1 | _ | |a Brück, Oscar |b 36 |
| 700 | 1 | _ | |a Bader, Gary D. |b 37 |
| 700 | 1 | _ | |a Wang, Bo |0 P:(DE-HGF)0 |b 38 |e Corresponding author |
| 773 | _ | _ | |a 10.48550/arXiv.2308.05864 |y 2023 |t arxiv |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/1015298/files/2308.05864.pdf |y OpenAccess |
| 909 | C | O | |o oai:juser.fz-juelich.de:1015298 |p openaire |p open_access |p driver |p VDB |p ec_fundedresources |p dnbdelivery |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 12 |6 P:(DE-Juel1)177675 |
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| 913 | 1 | _ | |a DE-HGF |b Key Technologies |l Natural, Artificial and Cognitive Information Processing |1 G:(DE-HGF)POF4-520 |0 G:(DE-HGF)POF4-525 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-500 |4 G:(DE-HGF)POF |v Decoding Brain Organization and Dysfunction |9 G:(DE-HGF)POF4-5254 |x 0 |
| 914 | 1 | _ | |y 2023 |
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| 920 | _ | _ | |l yes |
| 920 | 1 | _ | |0 I:(DE-Juel1)INM-1-20090406 |k INM-1 |l Strukturelle und funktionelle Organisation des Gehirns |x 0 |
| 980 | _ | _ | |a preprint |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a I:(DE-Juel1)INM-1-20090406 |
| 980 | _ | _ | |a UNRESTRICTED |
| 980 | 1 | _ | |a FullTexts |
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