Impact of Training Data, Ground Truth and Shape Variability in the Deep Learning-Based Semantic Segmentation of HeLa Cells Observed with Electron Microscopy

This paper investigates the impact of the amount of training data and the shape variability on the segmentation provided by the deep learning architecture U-Net. Further, the correctness of ground truth (GT) was also evaluated. The input data consisted of a three-dimensional set of images of HeLa ce...

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Detalles Bibliográficos
Autores principales: Karabağ, Cefa, Ortega-Ruíz, Mauricio Alberto, Reyes-Aldasoro, Constantino Carlos
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10058680/
https://www.ncbi.nlm.nih.gov/pubmed/36976110
http://dx.doi.org/10.3390/jimaging9030059

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10058680/
https://www.ncbi.nlm.nih.gov/pubmed/36976110
http://dx.doi.org/10.3390/jimaging9030059

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