nucleAIzer: A Parameter-free Deep Learning Framework for Nucleus Segmentation Using Image Style Transfer

Single-cell segmentation is typically a crucial task of image-based cellular analysis. We present nucleAIzer, a deep-learning approach aiming toward a truly general method for localizing 2D cell nuclei across a diverse range of assays and light microscopy modalities. We outperform the 739 methods su...

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Detalles Bibliográficos
Autores principales: Hollandi, Reka, Szkalisity, Abel, Toth, Timea, Tasnadi, Ervin, Molnar, Csaba, Mathe, Botond, Grexa, Istvan, Molnar, Jozsef, Balind, Arpad, Gorbe, Mate, Kovacs, Maria, Migh, Ede, Goodman, Allen, Balassa, Tamas, Koos, Krisztian, Wang, Wenyu, Caicedo, Juan Carlos, Bara, Norbert, Kovacs, Ferenc, Paavolainen, Lassi, Danka, Tivadar, Kriston, Andras, Carpenter, Anne Elizabeth, Smith, Kevin, Horvath, Peter
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8247631/
https://www.ncbi.nlm.nih.gov/pubmed/34222682
http://dx.doi.org/10.1016/j.cels.2020.04.003

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8247631/
https://www.ncbi.nlm.nih.gov/pubmed/34222682
http://dx.doi.org/10.1016/j.cels.2020.04.003

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