Department of Electronic Science, Xiamen University, Xiamen 361005, China.
Biosensors (Basel). 2024 Jul 26;14(8):363. doi: 10.3390/bios14080363.
Surface plasmon microscopy proves to be a potent tool for capturing interferometric scattering imaging data of individual particles at both micro and nanoscales, offering considerable potential for label-free analysis of bio-particles and bio-molecules such as exosomes, viruses, and bacteria. However, the manual analysis of acquired images remains a challenge, particularly when dealing with dense samples or strong background noise, common in practical measurements. Manual analysis is not only prone to errors but is also time-consuming, especially when handling a large volume of experimental images. Currently, automated methods for sensing and analysis of such data are lacking. In this paper, we develop an accelerated approach for surface plasmon microscopy imaging of individual particles based on combining the interference scattering model of single particle and deep learning processing. We create hybrid datasets by combining the theoretical simulation of particle images with the actual measurements. Subsequently, we construct a neural network utilizing the EfficientNet architecture. Our results demonstrate the effectiveness of this novel deep learning technique in classifying interferometric scattering images and identifying multiple particles under noisy conditions. This advancement paves the way for practical bio-applications through efficient automated particle analysis.
表面等离子体显微镜被证明是一种强大的工具，可以捕获单个粒子在微观和纳米尺度上的干涉散射成像数据，为生物粒子和生物分子（如外泌体、病毒和细菌）的无标记分析提供了巨大的潜力。然而，获取图像的手动分析仍然是一个挑战，特别是在处理密集样本或强背景噪声时，这在实际测量中很常见。手动分析不仅容易出错，而且非常耗时，尤其是在处理大量实验图像时。目前，缺乏用于此类数据的自动传感和分析方法。在本文中，我们基于将单个粒子的干涉散射模型与深度学习处理相结合，开发了一种用于单个粒子表面等离子体显微镜成像的加速方法。我们通过将粒子图像的理论模拟与实际测量相结合来创建混合数据集。随后，我们利用 EfficientNet 架构构建了一个神经网络。我们的结果表明，这种新的深度学习技术在嘈杂条件下对干涉散射图像进行分类和识别多个粒子的有效性。这一进展通过高效的自动粒子分析为实际的生物应用铺平了道路。
