Cargando…
Recurrent neural network-based volumetric fluorescence microscopy
Volumetric imaging of samples using fluorescence microscopy plays an important role in various fields including physical, medical and life sciences. Here we report a deep learning-based volumetric image inference framework that uses 2D images that are sparsely captured by a standard wide-field fluor...
Autores principales: | Huang, Luzhe, Chen, Hanlong, Luo, Yilin, Rivenson, Yair, Ozcan, Aydogan |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7985192/ https://www.ncbi.nlm.nih.gov/pubmed/33753716 http://dx.doi.org/10.1038/s41377-021-00506-9 |
Ejemplares similares
-
Fourier Imager Network (FIN): A deep neural network for hologram reconstruction with superior external generalization
por: Chen, Hanlong, et al.
Publicado: (2022) -
Neural network-based image reconstruction in swept-source optical coherence tomography using undersampled spectral data
por: Zhang, Yijie, et al.
Publicado: (2021) -
Phase recovery and holographic image reconstruction using deep learning in neural networks
por: Rivenson, Yair, et al.
Publicado: (2018) -
Sparsity-based multi-height phase recovery in holographic microscopy
por: Rivenson, Yair, et al.
Publicado: (2016) -
Design of task-specific optical systems using broadband diffractive neural networks
por: Luo, Yi, et al.
Publicado: (2019)