Improving axial resolution in Structured Illumination Microscopy using deep learning.
Philos Trans A Math Phys Eng Sci
; 379(2199): 20200298, 2021 Jun 14.
Article
en En
| MEDLINE
| ID: mdl-33896203
ABSTRACT
Structured Illumination Microscopy (SIM) is a widespread methodology to image live and fixed biological structures smaller than the diffraction limits of conventional optical microscopy. Using recent advances in image up-scaling through deep learning models, we demonstrate a method to reconstruct 3D SIM image stacks with twice the axial resolution attainable through conventional SIM reconstructions. We further demonstrate our method is robust to noise and evaluate it against two-point cases and axial gratings. Finally, we discuss potential adaptions of the method to further improve resolution. This article is part of the Theo Murphy meeting issue 'Super-resolution structured illumination microscopy (part 1)'.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Aprendizaje Profundo
/
Microscopía Fluorescente
Tipo de estudio:
Evaluation_studies
Límite:
Animals
/
Humans
Idioma:
En
Revista:
Philos Trans A Math Phys Eng Sci
Asunto de la revista:
BIOFISICA
/
ENGENHARIA BIOMEDICA
Año:
2021
Tipo del documento:
Article
País de afiliación:
Reino Unido