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Title: Deep learning 2D and 3D optical sectioning microscopy using cross-modality Pix2Pix cGAN image translation

Structured illumination microscopy (SIM) reconstructs optically-sectioned images of a sample from multiple spatially-patterned wide-field images, but the traditional single non-patterned wide-field images are more inexpensively obtained since they do not require generation of specialized illumination patterns. In this work, we translated wide-field fluorescence microscopy images to optically-sectioned SIM images by a Pix2Pix conditional generative adversarial network (cGAN). Our model shows the capability of both 2D cross-modality image translation from wide-field images to optical sections, and further demonstrates potential to recover 3D optically-sectioned volumes from wide-field image stacks. The utility of the model was tested on a variety of samples including fluorescent beads and fresh human tissue samples.

 
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Award ID(s):
2136744
NSF-PAR ID:
10308034
Author(s) / Creator(s):
; ; ;
Publisher / Repository:
Optical Society of America
Date Published:
Journal Name:
Biomedical Optics Express
Volume:
12
Issue:
12
ISSN:
2156-7085
Page Range / eLocation ID:
Article No. 7526
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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  2. Summary Lay Description

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