Improving fluorescence lifetime imaging microscopy phasor accuracy using convolutional neural networks

Improving fluorescence lifetime imaging microscopy phasor accuracy using convolutional neural networks

Introduction: Although a powerful biological imaging technique, fluorescence lifetime imaging microscopy (FLIM) faces challenges such as a slow acquisition rate, a low signal-to-noise ratio (SNR), and high cost and complexity. To address the fundamental problem of low SNR in FLIM images, we demonstr...

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Bibliographic Details
Main Authors: Varun Mannam, Jacob P. Brandt, Cody J. Smith, Xiaotong Yuan, Scott Howard
Format: Article
Language:English
Published: Frontiers Media S.A. 2023-12-01
Series:Frontiers in Bioinformatics
Subjects:
phasor lifetime synthesis
fluorescence lifetime imaging microscopy (FLIM)
lifetime image analysis
convolutional neural networks (CNNs)
phasor clustering method
image segmentation
Online Access:https://www.frontiersin.org/articles/10.3389/fbinf.2023.1335413/full

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https://www.frontiersin.org/articles/10.3389/fbinf.2023.1335413/full

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