In and out of the nucleus
- Resource Type
- Authors
- Tarek M. Zikry; Katarzyna M. Kedziora; Michael R. Kosorok; Jeremy E. Purvis
- Source
- PEARC
- Subject
- Fluorescence-lifetime imaging microscopy
education.field_of_study
Computer science
business.industry
Deep learning
Population
Pattern recognition
Convolutional neural network
medicine.anatomical_structure
Region of interest
medicine
Segmentation
Artificial intelligence
Kinase activity
education
business
Nucleus
- Language
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 model provided high-quality segmentation results that allowed us to accurately quantify the extent of cyclin-dependent kinase 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 will be free to increase the number of biosensors measured in single cells or reduce the phototoxicity of cellular imaging.