SHAPR predicts 3D cell shapes from 2D microscopic images

Reconstruction of shapes and sizes of three-dimensional (3D) objects from two- dimensional (2D) information is an intensely studied subject in computer vision. We here consider the level of single cells and nuclei and present a neural network-based SHApe PRediction autoencoder. For proof-of-concept,...

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Detalles Bibliográficos
Autores principales: Waibel, Dominik J.E., Kiermeyer, Niklas, Atwell, Scott, Sadafi, Ario, Meier, Matthias, Marr, Carsten
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9593790/
https://www.ncbi.nlm.nih.gov/pubmed/36304119
http://dx.doi.org/10.1016/j.isci.2022.105298

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9593790/
https://www.ncbi.nlm.nih.gov/pubmed/36304119
http://dx.doi.org/10.1016/j.isci.2022.105298

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