Enhanced plasmonic scattering imaging via deep learning-based super-resolution reconstruction for exosome imaging.

Abstract

Exosome analysis plays pivotal roles in various physiological and pathological processes. Plasmonic scattering microscopy (PSM) has proven to be an excellent label-free imaging platform for exosome detection. However, accurately detecting images scattered from exosomes remains a challenging task due to noise interference. Herein, we proposed an image processing strategy based on a new blind super-resolution deep learning neural network, named ESRGAN-SE, to improve the resolution of exosome PSI images. This model can obtain super-resolution reconstructed images without increasing experimental complexity. The trained model can directly generate high-resolution plasma scattering images from low-resolution images collected in experiments. The results of experiments involving the detection of light scattered by exosomes showed that the proposed super-resolution detection method has strong generalizability and robustness. Moreover, ESRGAN-SE achieved excellent results of 35.52036, 0.09081, and 8.13176 in terms of three reference-free image quality assessment metrics, respectively. These results show that the proposed network can effectively reduce image information loss, enhance mutual information between pixels, and decrease feature differentiation. And, the single-image SNR evaluation score of 3.93078 also showed that the distinction between the target and the background was significant. The suggested model lays the foundation for a potentially successful approach to imaging analysis. This approach has the potential to greatly improve the accuracy and efficiency of exosome analysis, leading to more accurate cancer diagnosis and potentially improving patient outcomes.