Spatial resolution improved fluorescence lifetime imaging via deep learning

Xiao, Dong; Zang, Zhenya; Xie, Wujun; Sapermsap, Natakorn; Chen, Yu; Li, David

doi:10.1364/oe.451215

Cited by 9 publications

(6 citation statements)

References 25 publications

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“…DL methods for spatial resolution enhancement of FLIM images are also developed, including SRI-FLIMnet for reconstructing high-resolution images from lowresolution 3D FLIM data [89] and CNN-based denoising method removing noise in phasor plots after the K-means clustering segmentation [90].…”

Section: Deep Learning (Dl)mentioning

confidence: 99%

Lifetime Determination Algorithms for Time-Domain Fluorescence Lifetime Imaging: A Review

Li¹,

Liu²,

Xiao³

et al. 2023

Fluorescence Imaging - Recent Advances and Applications

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Fluorescence lifetime imaging (FLIM) is powerful for monitoring cellular microenvironments, protein conformational changes, and protein interactions. It can facilitate metabolism research, drug screening, DNA sequencing, and cancer diagnosis. Lifetime determination algorithms (LDAs) adopted in FLIM analysis can influence biological interpretations and clinical diagnoses. Herein, we discuss the commonly used and advanced time-domain LDAs classified in fitting and non-fitting categories. The concept and explicit mathematical forms of LDAs are reviewed. The output lifetime parameter types are discussed, including lifetime components, average lifetimes, and graphic representation. We compare their performances, identify trends, and provide suggestions for end users in terms of multi-exponential decay unmixing ability, lifetime estimation precision, and processing speed.

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Section: Deep Learning (Dl)mentioning

confidence: 99%

Lifetime Determination Algorithms for Time-Domain Fluorescence Lifetime Imaging: A Review

Li¹,

Liu²,

Xiao³

et al. 2023

Fluorescence Imaging - Recent Advances and Applications

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“…Jason et al proposed a hybrid CNN architecture [34] to extract individual lifetime components from multi-exponential, hyperspectral fluorescence emission decays. To enhance the spatial resolution of FLIM images, Dong et al [35] introduced a cascade CNN architecture to infer fluorescence lifetime and improve the spatial resolution afterwards. An Extreme Learning Machine (ELM) [36] was presented to achieve fast and accurate lifetimes reconstruction with back-propagation-free, online training.…”

Section: Deep Learning For Flimmentioning

confidence: 99%

Compact and robust deep learning architecture for fluorescence lifetime imaging and FPGA implementation

Zang

Xiao

Wang

et al. 2023

Methods Appl. Fluoresc.

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This paper reports a bespoke adder-based deep learning network for time-domain fluorescence lifetime imaging (FLIM). By leveraging the l1-norm extraction method, we propose a 1-D Fluorescence Lifetime AdderNet (FLAN) without multiplication-based convolutions to reduce the computational complexity. Further, we compressed fluorescence decays in temporal dimension using a log-scale merging technique to discard redundant temporal information derived as log-scaling FLAN (FLAN+LS). FLAN+LS achieves 0.11 and 0.23 compression ratios compared with FLAN and a conventional 1-D convolutional neural network (1-D CNN) while maintaining high accuracy in retrieving lifetimes. We extensively evaluated FLAN and FLAN+LS using synthetic and real data. A traditional fitting method and other non-fitting, high-accuracy algorithms were compared with our networks for synthetic data. Our networks attained a minor reconstruction error in different photon-count scenarios. For real data, we used fluorescent beads’ data acquired by a confocal microscope to validate the effectiveness of real fluorophores, and our networks can differentiate beads with different lifetimes. Additionally, we implemented the network architecture on a field-programmable gate array (FPGA) with a post-quantization technique to shorten the bit-width, thereby improving computing efficiency. FLAN+LS on hardware achieves the highest computing efficiency compared to 1-D CNN and FLAN. We also discussed the applicability of our network and hardware architecture for other time-resolved biomedical applications using photon-efficient, time-resolved sensors.

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“…PCA (84), partial least squares discriminant analysis (PLS-DA) (85,86), K-nearest neighbors (87), support vector machine (88), hierarchical clustering (89), and fuzzy clustering (90) also have been widely used. Pattern recognition and classification methods based on artificial neural networks (ANNs) (91) and multilayer networks for deep learning (92) are the most important tools currently being used in this area.…”

Section: Pattern Recognition and Classificationmentioning

confidence: 99%

Photoluminescence Probes in Data-Enabled Sensing

Suskil

Murray

Sanap

et al. 2023

Annual Rev. Anal. Chem.

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This review summarizes the current status of development in photoluminescent probes, multidimensional photoluminescence detection, and multivariate data analysis methods. It then highlights reports featuring multivariate analysis of multidimensional measurements of photoluminescent probes published between June 2015 and June 2022, emphasizing work in the last 5 years. Important trends include the development of probe arrays, which provide fingerprint responses to the analyte(s) of interest and facilitate the analysis of complex samples; the application of neural networks and deep learning to pattern recognition and feature selection in photoluminescence images; and the application of multiway multivariate analysis to mining matrices, three-way arrays, and higher-order measurements, including hyperspectral intensity and lifetime images. These examples illustrate the increase in information extraction provided by the combination of multidimensional measurements and multivariate analysis. Expected final online publication date for the Annual Review of Analytical Chemistry, Volume 16 is June 2023. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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Spatial resolution improved fluorescence lifetime imaging via deep learning

Cited by 9 publications

References 25 publications

Lifetime Determination Algorithms for Time-Domain Fluorescence Lifetime Imaging: A Review

Lifetime Determination Algorithms for Time-Domain Fluorescence Lifetime Imaging: A Review

Compact and robust deep learning architecture for fluorescence lifetime imaging and FPGA implementation

Photoluminescence Probes in Data-Enabled Sensing

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