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The multimodality cell segmentation challenge: toward universal solutions.

Authors :
Ma J
Xie R
Ayyadhury S
Ge C
Gupta A
Gupta R
Gu S
Zhang Y
Lee G
Kim J
Lou W
Li H
Upschulte E
Dickscheid T
de Almeida JG
Wang Y
Han L
Yang X
Labagnara M
Gligorovski V
Scheder M
Rahi SJ
Kempster C
Pollitt A
Espinosa L
Mignot T
Middeke JM
Eckardt JN
Li W
Li Z
Cai X
Bai B
Greenwald NF
Van Valen D
Weisbart E
Cimini BA
Cheung T
Brück O
Bader GD
Wang B
Source :
Nature methods [Nat Methods] 2024 Jun; Vol. 21 (6), pp. 1103-1113. Date of Electronic Publication: 2024 Mar 26.
Publication Year :
2024

Abstract

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.<br /> (© 2024. The Author(s), under exclusive licence to Springer Nature America, Inc.)

Details

Language :
English
ISSN :
1548-7105
Volume :
21
Issue :
6
Database :
MEDLINE
Journal :
Nature methods
Publication Type :
Academic Journal
Accession number :
38532015
Full Text :
https://doi.org/10.1038/s41592-024-02233-6