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Title: In and out of the nucleus: CNN based segmentation of cell nuclei from images of a translocating sensor
This study demonstrates application of convolutional neural networks (CNNs) for the analysis of a unique image analysis problem in fluorescence microscopy. We employed the U-Net CNN architecture and trained a model to segment nuclear regions in images of a translocating biosensor—which alternates between the nucleus and cytoplasm—without the need for a constant nuclear marker. The modelprovided high-quality segmentation results that allowed us to accurately quantify the extent of cyclin-dependentkinase activity in a population of cells. We envision that the development of this kind of analysis tools will enable biologists to design live-cell fluorescence imaging experiments without the need for providing a constant marker for a subcellular region of interest. As a consequence, they willbe free to increase the number of biosensors measured in single cells or reduce the phototoxicity of cellular imaging.  more » « less
Award ID(s):
1845796
PAR ID:
10126159
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
Practice and Experience in Advanced Research Computing
Page Range / eLocation ID:
1 to 4
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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