The multimodality cell segmentation challenge : toward universal solutions
© 2024. The Author(s), under exclusive licence to Springer Nature America, Inc..
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 hyper-parameters in different experimental settings. Here, we present a multimodality cell segmentation benchmark, comprising more than 1,500 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.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2024 |
|---|---|
| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - year:2024 |
|---|---|
| Enthalten in: |
Nature methods - (2024) vom: 26. März |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Ma, Jun [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
|---|
| Anmerkungen: |
Date Revised 31.03.2024 published: Print-Electronic Citation Status Publisher |
|---|
| doi: |
10.1038/s41592-024-02233-6 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM370215648 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM370215648 | ||
| 003 | DE-627 | ||
| 005 | 20240401232900.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 240328s2024 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1038/s41592-024-02233-6 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1359.xml |
| 035 | |a (DE-627)NLM370215648 | ||
| 035 | |a (NLM)38532015 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Ma, Jun |e verfasserin |4 aut | |
| 245 | 1 | 4 | |a The multimodality cell segmentation challenge |b toward universal solutions |
| 264 | 1 | |c 2024 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Revised 31.03.2024 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status Publisher | ||
| 520 | |a © 2024. The Author(s), under exclusive licence to Springer Nature America, Inc. | ||
| 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 hyper-parameters in different experimental settings. Here, we present a multimodality cell segmentation benchmark, comprising more than 1,500 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 | ||
| 650 | 4 | |a Journal Article | |
| 700 | 1 | |a Xie, Ronald |e verfasserin |4 aut | |
| 700 | 1 | |a Ayyadhury, Shamini |e verfasserin |4 aut | |
| 700 | 1 | |a Ge, Cheng |e verfasserin |4 aut | |
| 700 | 1 | |a Gupta, Anubha |e verfasserin |4 aut | |
| 700 | 1 | |a Gupta, Ritu |e verfasserin |4 aut | |
| 700 | 1 | |a Gu, Song |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Yao |e verfasserin |4 aut | |
| 700 | 1 | |a Lee, Gihun |e verfasserin |4 aut | |
| 700 | 1 | |a Kim, Joonkee |e verfasserin |4 aut | |
| 700 | 1 | |a Lou, Wei |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Haofeng |e verfasserin |4 aut | |
| 700 | 1 | |a Upschulte, Eric |e verfasserin |4 aut | |
| 700 | 1 | |a Dickscheid, Timo |e verfasserin |4 aut | |
| 700 | 1 | |a de Almeida, José Guilherme |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Yixin |e verfasserin |4 aut | |
| 700 | 1 | |a Han, Lin |e verfasserin |4 aut | |
| 700 | 1 | |a Yang, Xin |e verfasserin |4 aut | |
| 700 | 1 | |a Labagnara, Marco |e verfasserin |4 aut | |
| 700 | 1 | |a Gligorovski, Vojislav |e verfasserin |4 aut | |
| 700 | 1 | |a Scheder, Maxime |e verfasserin |4 aut | |
| 700 | 1 | |a Rahi, Sahand Jamal |e verfasserin |4 aut | |
| 700 | 1 | |a Kempster, Carly |e verfasserin |4 aut | |
| 700 | 1 | |a Pollitt, Alice |e verfasserin |4 aut | |
| 700 | 1 | |a Espinosa, Leon |e verfasserin |4 aut | |
| 700 | 1 | |a Mignot, Tâm |e verfasserin |4 aut | |
| 700 | 1 | |a Middeke, Jan Moritz |e verfasserin |4 aut | |
| 700 | 1 | |a Eckardt, Jan-Niklas |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Wangkai |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Zhaoyang |e verfasserin |4 aut | |
| 700 | 1 | |a Cai, Xiaochen |e verfasserin |4 aut | |
| 700 | 1 | |a Bai, Bizhe |e verfasserin |4 aut | |
| 700 | 1 | |a Greenwald, Noah F |e verfasserin |4 aut | |
| 700 | 1 | |a Van Valen, David |e verfasserin |4 aut | |
| 700 | 1 | |a Weisbart, Erin |e verfasserin |4 aut | |
| 700 | 1 | |a Cimini, Beth A |e verfasserin |4 aut | |
| 700 | 1 | |a Cheung, Trevor |e verfasserin |4 aut | |
| 700 | 1 | |a Brück, Oscar |e verfasserin |4 aut | |
| 700 | 1 | |a Bader, Gary D |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Bo |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Nature methods |d 2004 |g (2024) vom: 26. März |w (DE-627)NLM154352349 |x 1548-7105 |7 nnns |
| 773 | 1 | 8 | |g year:2024 |g day:26 |g month:03 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1038/s41592-024-02233-6 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |j 2024 |b 26 |c 03 | ||