Machine learning for faster and smarter fluorescence lifetime imaging microscopy

@article{Mannam2020MachineLF,
  title={Machine learning for faster and smarter fluorescence lifetime imaging microscopy},
  author={Varun Mannam and Yide Zhang and Xiaotong Yuan and Cara Ravasio and Scott S. Howard},
  journal={Journal of Physics: Photonics},
  year={2020},
  volume={2}
}
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… 
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16 Citations

Fluorescence lifetime image microscopy prediction with convolutional neural networks for cell detection and classification in tissues

It is demonstrated that CNNs can achieve high accuracy in cell detection and classification without large amounts of data when applied to histology images acquired by fluorescence lifetime imaging microscopy (FLIM).

Convolutional neural network denoising in fluorescence lifetime imaging microscopy (FLIM)

The proposed deep learning-based workflow provides fast and accurate automatic segmentation of fluorescence images using instant FLIM, and the denoising operation is effective for the segmentation if the FLIM measurements are noisy.

Low dosage 3D volume fluorescence microscopy imaging using compressive sensing

A compressive sensing (CS) based approach is presented to fully reconstruct 3D volumes with the same signal-to-noise ratio (SNR) with less than half of the excitation dosage to reduce sample photo-toxicity.

Deep learning-based super-resolution fluorescence microscopy on small datasets

A new convolutional neural network based approach that is successfully trained with small datasets and achieves super-resolution images from this small dataset can be applied to other biomedical imaging modalities such as MRI and X-ray imaging, where obtaining large training datasets is challenging.

Phasor-based image segmentation: machine learning clustering techniques

The work on using machine learning clustering techniques to establish an unsupervised and automatic method that can be used for identifying populations of fluorescent species in spectral and lifetime imaging is presented.

Phasor-based image segmentation: machine learning clustering techniques.

This work presents the work on using machine learning clustering techniques to establish an unsupervised and automatic method that can be used for identifying populations of fluorescent species in spectral and lifetime imaging.

In vitro and in vivo NIR Fluorescence Lifetime Imaging with a time-gated SPAD camera

In vivo NIR MFLI measurement with SwissSPAD2, a large time-gated SPAD camera, demonstrates that SPAD cameras offer a powerful technology for in vivo preclinical applications in the NIR window.

Small Training Dataset Convolutional Neural Networks for Application Specific Super-Resolution Microscopy

This approach shows that microscopy applications can use DenseED blocks to train on smaller datasets that are application-specific imaging platforms and there is a promise for applying this to other imaging modalities such as MRI/X-ray, etc.

Real-time Image Denoising of Mixed Poisson-Gaussian Noise in Fluorescence Microscopy Images using ImageJ

Fluorescence microscopy imaging speed is fundamentally limited by the measurement signal-to-noise ratio (SNR). To improve image SNR for a given image acquisition rate, computational denoising…

Comprehensive Investigation of Parameters Influencing Fluorescence Lifetime Imaging Microscopy in Frequency- and Time-Domain Illustrated by Phasor Plot Analysis

Having access to fluorescence lifetime, researchers can reveal in-depth details about the microenvironment as well as the physico-chemical state of the molecule under investigation. However, the high…

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