Structure preserving adversarial generation of labeled training samples for single-cell segmentation

We introduce a generative data augmentation strategy to improve the accuracy of instance segmentation of microscopy data for complex tissue structures. Our pipeline uses regular and conditional generative adversarial networks (GANs) for image-to-image translation to construct synthetic microscopy im...

Descripción completa

Detalles Bibliográficos
Autores principales: Tasnadi, Ervin, Sliz-Nagy, Alex, Horvath, Peter
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10545934/
https://www.ncbi.nlm.nih.gov/pubmed/37725984
http://dx.doi.org/10.1016/j.crmeth.2023.100592

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10545934/
https://www.ncbi.nlm.nih.gov/pubmed/37725984
http://dx.doi.org/10.1016/j.crmeth.2023.100592

Ejemplares similares