Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning

A major challenge in the analysis of tissue imaging data is cell segmentation, the task of identifying the precise boundary of every cell in an image. To address this problem we constructed TissueNet, a dataset for training segmentation models that contains more than 1 million manually labeled cells...

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Detalles Bibliográficos
Autores principales: Greenwald, Noah F., Miller, Geneva, Moen, Erick, Kong, Alex, Kagel, Adam, Dougherty, Thomas, Fullaway, Christine Camacho, McIntosh, Brianna J., Leow, Ke Xuan, Schwartz, Morgan Sarah, Pavelchek, Cole, Cui, Sunny, Camplisson, Isabella, Bar-Tal, Omer, Singh, Jaiveer, Fong, Mara, Chaudhry, Gautam, Abraham, Zion, Moseley, Jackson, Warshawsky, Shiri, Soon, Erin, Greenbaum, Shirley, Risom, Tyler, Hollmann, Travis, Bendall, Sean C., Keren, Leeat, Graf, William, Angelo, Michael, Van Valen, David
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9010346/
https://www.ncbi.nlm.nih.gov/pubmed/34795433
http://dx.doi.org/10.1038/s41587-021-01094-0

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9010346/
https://www.ncbi.nlm.nih.gov/pubmed/34795433
http://dx.doi.org/10.1038/s41587-021-01094-0

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