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Machine learning for faster and smarter fluorescence lifetime imaging microscopy

Mannam, Varun and Zhang, Yide and Yuan, Xiaotong and Ravasio, Cara and Howard, Scott S. (2020) Machine learning for faster and smarter fluorescence lifetime imaging microscopy. Journal of Physics: Photonics, 2 (4). Art. No. 042005. ISSN 2515-7647. https://resolver.caltech.edu/CaltechAUTHORS:20200925-135425089

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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.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1088/2515-7647/abac1aDOIArticle
https://arxiv.org/abs/2008.02320arXivDiscussion Paper
ORCID:
AuthorORCID
Mannam, Varun0000-0002-1866-6092
Zhang, Yide0000-0002-9463-3970
Howard, Scott S.0000-0003-3246-6799
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.
Funders:
Funding AgencyGrant Number
Berry Family FoundationUNSPECIFIED
University of Notre DameUNSPECIFIED
NSFCBET-1554516
Subject Keywords:microscopy, fluorescence lifetime imaging microscopy, machine learning, convolutional neural network, deep learning, classification, segmentation
Issue or Number:4
Record Number:CaltechAUTHORS:20200925-135425089
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20200925-135425089
Official Citation:Varun Mannam et al 2020 J. Phys. Photonics 2 042005
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:105563
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:25 Sep 2020 21:55
Last Modified:25 Sep 2020 21:55

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