Machine learning for faster and smarter fluorescence lifetime imaging microscopy
Abstract
Fluorescence lifetime imaging microscopy (FLIM) is a powerful technique in biomedical research that uses the fluorophore decay rate to provide additional contrast in fluorescence microscopy. However, at present, the calculation, analysis, and interpretation of FLIM is a complex, slow, and computationally expensive process. Machine learning (ML) techniques are well suited to extract and interpret measurements from multi-dimensional FLIM data sets with substantial improvement in speed over conventional methods. In this topical review, we first discuss the basics of FILM and ML. Second, we provide a summary of lifetime extraction strategies using ML and its applications in classifying and segmenting FILM images with higher accuracy compared to conventional methods. Finally, we discuss two potential directions to improve FLIM with ML with proof of concept demonstrations.
Additional Information
© 2020 The Author(s). Published by IOP Publishing Ltd. Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. Received 31 March 2020. Accepted 4 August 2020. Accepted Manuscript online 4 August 2020. Published 22 September 2020. Yide Zhang's research was supported by the Berry Family Foundation Graduate Fellowship of Advanced Diagnostics & Therapeutics (AD&T), University of Notre Dame. The authors acknowledge the Notre Dame Integrated Imaging Facility (NDIIF) for the use of the Nikon A1R-MP confocal microscope and Nikon Eclipse 90i widefield microscope in NDIIF's Optical Microscopy Core. The authors further acknowledge the Notre Dame Center for Research Computing (CRC) for providing the Nvidia GeForce GTX 1080-Ti GPU resources for training the neural networks using the Fluorescence Microscopy Denoising (FMD) dataset in TensorFlow. Funding information: This material is based upon work supported by the National Science Foundation (NSF) under Grant No. CBET-1554516. Disclosures: The authors declare no conflicts of interest.Attached Files
Published - Mannam_2020_J._Phys._Photonics_2_042005.pdf
Accepted Version - 2008.02320.pdf
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Additional details
- Eprint ID
- 105563
- Resolver ID
- CaltechAUTHORS:20200925-135425089
- Berry Family Foundation
- University of Notre Dame
- CBET-1554516
- NSF
- Created
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2020-09-25Created from EPrint's datestamp field
- Updated
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2021-11-16Created from EPrint's last_modified field