Projects per year
Abstract / Description of output
Many medical imaging modalities have benefited from recent advances in Machine Learning (ML), specifically in deep learning, such as neural networks. Computers can be trained to investigate and enhance medical imaging methods without using valuable human resources. In recent years, Fluorescence Lifetime Imaging (FLIm) has received increasing attention from the ML community. FLIm goes beyond conventional spectral imaging, providing additional lifetime information, and could lead to optical histopathology supporting real-time diagnostics. However, most current studies do not use the full potential of machine/deep learning models. As a developing image modality, FLIm data are not easily obtainable, which, coupled with an absence of standardisation, is pushing back the research to develop models which could advance automated diagnosis and help promote FLIm.
In this paper, we describe recent developments that improve FLIm image quality, specifically time-domain systems, and we summarise sensing, signal-to-noise analysis and the advances in registration and low-level tracking. We review the two main applications of ML for FLIm: lifetime estimation and image analysis through classification and segmentation. We suggest a course of action to improve the quality of ML studies applied to FLIm. Our final goal is to promote FLIm and attract more ML practitioners to explore the potential of lifetime imaging.
In this paper, we describe recent developments that improve FLIm image quality, specifically time-domain systems, and we summarise sensing, signal-to-noise analysis and the advances in registration and low-level tracking. We review the two main applications of ML for FLIm: lifetime estimation and image analysis through classification and segmentation. We suggest a course of action to improve the quality of ML studies applied to FLIm. Our final goal is to promote FLIm and attract more ML practitioners to explore the potential of lifetime imaging.
Original language | English |
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Article number | 022001 |
Journal | Methods and Applications in Fluorescence |
Volume | 12 |
Issue number | 2 |
Early online date | 6 Dec 2023 |
DOIs | |
Publication status | Published - 1 Apr 2024 |
Keywords / Materials (for Non-textual outputs)
- FLIm
- biomedical engineering
- deep learning
- fluorescence lifetime imaging
- machine learning
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Towards in-vivo in-situ early lung cancer diagnosis using fluorescence lifetime imaging microscopy: a preliminary study on data-driven histological synthesis from label-free autofluorescence lifetime images on ex-vivo lung tissue
Wang, Q., Hopgood, J., Akram, A. & Vallejo, M.
1/10/22 → 31/01/24
Project: Research
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Next-generation sensing for human in vivo pharmacology- accelerating drug development in inflammatory diseases
1/10/19 → 30/09/22
Project: Research
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Deep learning-based virtual H&E staining from label-free autofluorescence lifetime images
Wang, Q., Akram, A. R., Dorward, D., Talas, S., Monks, B., Thum, C., Hopgood, J. R., Javidi, M. & Vallejo, M., 28 Jun 2024, (E-pub ahead of print) In: njp Imaging. 2, 1, p. 17Research output: Contribution to journal › Article › peer-review
Open AccessFile -
A novel fit-flexible fluorescence soft imager: Tri-sensing of intensity, fall-time, and life profile
Taimori, A., Mills, B., Gaughan, E., Ali, A., Dhaliwal, K., Williams, G., Finlayson, N. & Hopgood, J. R., 1 Jun 2024, In: IEEE Transactions on Biomedical Engineering. 71, 6, p. 1864-1878 14 p.Research output: Contribution to journal › Article › peer-review
Open AccessFile -
Bayesian Statistical Analysis for Bacterial Detection in Pulmonary Endomicroscopic Fluorescence Lifetime Imaging
Demirel, M., Mills, B., Gaughan, E., Dhaliwal, K. & Hopgood, J. R., 7 Feb 2024, (E-pub ahead of print) In: IEEE Transactions on Image Processing. 33, p. 1241-1256 14 p.Research output: Contribution to journal › Article › peer-review
Open AccessFile