UnMICST: Deep learning with real augmentation for robust segmentation of highly multiplexed images of human tissues

Upcoming technologies enable routine collection of highly multiplexed (20–60 channel), subcellular resolution images of mammalian tissues for research and diagnosis. Extracting single cell data from such images requires accurate image segmentation, a challenging problem commonly tackled with deep le...

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Detalles Bibliográficos
Autores principales: Yapp, Clarence, Novikov, Edward, Jang, Won-Dong, Vallius, Tuulia, Chen, Yu-An, Cicconet, Marcelo, Maliga, Zoltan, Jacobson, Connor A., Wei, Donglai, Santagata, Sandro, Pfister, Hanspeter, Sorger, Peter K.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9674686/
https://www.ncbi.nlm.nih.gov/pubmed/36400937
http://dx.doi.org/10.1038/s42003-022-04076-3

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9674686/
https://www.ncbi.nlm.nih.gov/pubmed/36400937
http://dx.doi.org/10.1038/s42003-022-04076-3

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