Convolutional Neural Network Denoising in Fluorescence Lifetime Imaging Microscopy (FLIM)

TL;DR

This paper presents a deep learning-based denoising method integrated with an instant FLIM system to improve image quality and segmentation accuracy in fluorescence lifetime imaging microscopy, enabling faster and more precise biomedical imaging.

Contribution

The study introduces a novel deep learning denoising approach combined with instant FLIM, enhancing SNR and segmentation accuracy in fluorescence lifetime imaging.

Findings

Deep learning denoising improves FLIM SNR.

Enhanced segmentation accuracy in noisy FLIM data.

Effective in vivo mouse kidney imaging results.

Abstract

Fluorescence lifetime imaging microscopy (FLIM) systems are limited by their slow processing speed, low signal-to-noise ratio (SNR), and expensive and challenging hardware setups. In this work, we demonstrate applying a denoising convolutional network to improve FLIM SNR. The network will be integrated with an instant FLIM system with fast data acquisition based on analog signal processing, high SNR using high-efficiency pulse-modulation, and cost-effective implementation utilizing off-the-shelf radio-frequency components. Our instant FLIM system simultaneously provides the intensity, lifetime, and phasor plots \textit{in vivo} and \textit{ex vivo}. By integrating image denoising using the trained deep learning model on the FLIM data, provide accurate FLIM phasor measurements are obtained. The enhanced phasor is then passed through the K-means clustering segmentation method, an unbiased and unsupervised machine learning technique to separate different fluorophores accurately. Our experimental \textit{in vivo} mouse kidney results indicate that introducing the deep learning image denoising model before the segmentation effectively removes the noise in the phasor compared to existing methods and provides clearer segments. Hence, the proposed deep learning-based workflow provides fast and accurate automatic segmentation of fluorescence images using instant FLIM. The denoising operation is effective for the segmentation if the FLIM measurements are noisy. The clustering can effectively enhance the detection of biological structures of interest in biomedical imaging applications.