Net-FLICS: fast quantitative wide-field fluorescence lifetime imaging with compressed sensing – a deep learning approach

Ruoyang Yao; Marien Ochoa; Pingkun Yan; Xavier Intes

doi:10.1038/s41377-019-0138-x

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Net-FLICS: fast quantitative wide-field fluorescence lifetime imaging with compressed sensing – a deep learning approach

Letters

- Net-FLICS: fast quantitative wide-field fluorescence lifetime imaging with compressed sensing – a deep learning approach
- Net-FLICS: fast quantitative wide-field fluorescence lifetime imaging with compressed sensing – a deep learning approach
- LSA 2019年8卷第2期页码：212-218
- Affiliations：
  
  Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
- Author bio：
  
  Xavier Intes (intesx@rpi.edu)
- Funds：
- DOI：10.1038/s41377-019-0138-x
  中图分类号：
- 纸质出版日期：2019，
  
  网络出版日期：2019-03-06，
  
  收稿日期：2018-10-04，
  
  修回日期：2019-02-06，
  
  录用日期：2019-02-13
- Accepted：
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Net-FLICS: fast quantitative wide-field fluorescence lifetime imaging with compressed sensing – a deep learning approach[J]. LSA, 2019,8(2):212-218.

Yao, R. Y. et al. Net-FLICS: fast quantitative wide-field fluorescence lifetime imaging with compressed sensing – a deep learning approach. Light: Science & Applications, 8, 212-218 (2019).
Net-FLICS: fast quantitative wide-field fluorescence lifetime imaging with compressed sensing – a deep learning approach[J]. LSA, 2019,8(2):212-218. DOI： 10.1038/s41377-019-0138-x.

Yao, R. Y. et al. Net-FLICS: fast quantitative wide-field fluorescence lifetime imaging with compressed sensing – a deep learning approach. Light: Science & Applications, 8, 212-218 (2019). DOI： 10.1038/s41377-019-0138-x.

摘要

Abstract

Macroscopic fluorescence lifetime imaging (MFLI) via compressed sensed (CS) measurements enables efficient and accurate quantification of molecular interactions in vivo over a large field of view (FOV). However

the current data-processing workflow is slow

complex and performs poorly under photon-starved conditions. In this paper

we propose Net-FLICS

a novel image reconstruction method based on a convolutional neural network (CNN)

to directly reconstruct the intensity and lifetime images from raw time-resolved CS data. By carefully designing a large simulated dataset

Net-FLICS is successfully trained and achieves outstanding reconstruction performance on both in vitro and in vivo experimental data and even superior results at low photon count levels for lifetime quantification.

关键词

Keywords

references

Marcu, L. Fluorescence lifetime techniques in medical applications.Ann. Biomed. Eng.40, 304-331 (2012)..

Hanley, Q. S., Arndt-Jovin, D. J.&Jovin, T. M. Spectrally resolved fluorescence lifetime imaging microscopy.Appl. Spectrosc.56, 155-166 (2002)..

Pian, Q. et al. Compressive hyperspectral time-resolved wide-field fluorescence lifetime imaging.Nat. Photon.11, 411-414 (2017)..

Donoho, D. L. Compressed sensing.IEEE Trans. Inf. Theory52, 1289-1306 (2006)..

LeCun, Y., Bengio, Y.&Hinton, G. Deep learning.Nature521, 436-444 (2015)..

Yang, Q. S. et al. Low-dose CT image denoising using a generative adversarial network with Wasserstein distance and perceptual loss.IEEE Trans. Med. Imaging37, 1348-1357 (2018)..

Krizhevsky, A., Sutskever, I.&Hinton, G. E. ImageNet classification with deep convolutional neural networks. InProceedings of the 25th International Conference on Neural Information Processing Systems1097-1105 (Curran Associates Inc., Lake Tahoe, 2012).

Dong, C. et al. Learning a deep convolutional network for image super-resolution. InProceedings of the 13th European Conference on Computer Vision184-199 (Springer, Zurich, 2014).

Kulkarni, K. et al. ReconNet: non-iterative reconstruction of images from compressively sensed measurements. InProceedings of the IEEE Conference on Computer Vision and Pattern Recognition449-458 (IEEE, Las Vegas, 2016).

Mousavi, A.&Baraniuk, R. G. Learning to invert: Signal recovery via deep convolutional networks. InProceedings of 2017 IEEE International Conference on Acoustics, Speech and Signal Processing2272-2276 (IEEE, New Orleans, 2017).

Yao, H. T. et al. DR2-net: deep residual reconstruction network for image compressive sensing.arXiv Prepr. arXiv1702, 05743 (2017)..

Greenspan, H., van Ginneken, B.&Summers, R. M. Guest editorial deep learning in medicalimaging: overview and future promise of an exciting new technique.IEEE Trans. Med. Imaging35, 1153-1159 (2016)..

Chen, C. L. et al. Deep learning in label-free cell classification.Sci. Rep.6, 21471 (2016)..

Rivenson, Y. et al. Deep learning microscopy.Optica4, 1437-1443 (2017)..

Li, C. B.Compressive sensing for 3D data processing tasks: applications, models and algorithms. PhD thesis, Rice University Houston, TX, USA (2011).

He, K. M. et al. Deep residual learning for image recognition. InProceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition770-778 (IEEE, Las Vegas, 2016).

Cohen, G. et al. EMNIST: an extension of MNIST to handwritten letters.arXiv Prepr. arXiv1702, 05373 (2017)..

Chollet, F. "Keras" (2015). (This reference cannot be found online. Please check)

Abadi, M. et al. Tensorflow: a system for large-scale machine learning. InProceedings of the 12th USENIX conference on Operating Systems Design and Implementation265-283 (USENIX Association, Savannah, 2016).

Ruder, S. An overview of gradient descent optimization algorithms.arXiv Prepr. arXiv1609, 04747 (2016)..

van der Maaten, L.&Hinton, G. Visualizing data using t-SNE.J. Mach. Learn. Res.9, 2579-2605 (2008)..

Ochoa, M. et al. Assessing patterns for compressive fluorescence lifetime imaging.Opt. Lett.43, 4370-4373 (2018)..

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