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Applications of machine learning in time-domain fluorescence lifetime imaging: a review
Methods and Applications in Fluorescence ( IF 3.2 ) Pub Date : 2024-02-08 , DOI: 10.1088/2050-6120/ad12f7 Dorian Gouzou , Ali Taimori , Tarek Haloubi , Neil Finlayson , Qiang Wang , James R Hopgood , Marta Vallejo
Methods and Applications in Fluorescence ( IF 3.2 ) Pub Date : 2024-02-08 , DOI: 10.1088/2050-6120/ad12f7 Dorian Gouzou , Ali Taimori , Tarek Haloubi , Neil Finlayson , Qiang Wang , James R Hopgood , Marta Vallejo
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.
更新日期：2024-02-08
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