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Fluorescence imaging has been used for decades to identify important elements in biological samples. However, the additional overhead of fixing and staining the sample limits the pervasiveness of the technique. Here we show a robust virtual fluorescence technique which uses deep learning to jointly optimize microscope design (i.e. illumination pattern) and fluorescence image reconstruction. Our results show how by combining the image capture and processing stages we can accurately and adaptively predict fluorescent images from unlabelled biological samples.
Colin L. Cooke,Pavan C. Konda,Kanghyun Kim, andRoarke W. Horstmeyer
"A fast and adaptable approach to virtual fluorescence microscopy", Proc. SPIE 11655, Label-free Biomedical Imaging and Sensing (LBIS) 2021, 1165504 (5 March 2021); https://doi.org/10.1117/12.2576712
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Colin L. Cooke, Pavan C. Konda, Kanghyun Kim, Roarke W. Horstmeyer, "A fast and adaptable approach to virtual fluorescence microscopy," Proc. SPIE 11655, Label-free Biomedical Imaging and Sensing (LBIS) 2021, 1165504 (5 March 2021); https://doi.org/10.1117/12.2576712