  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.
