Enhancing Nanoparticle Detection in Interferometric Scattering (iSCAT) Microscopy Using a Mask R-CNN
Enhancing Nanoparticle Detection in Interferometric Scattering (iSCAT) Microscopy Using a Mask R-CNN
复制标题
使用掩模 R-CNN 增强干涉散射 (iSCAT) 显微镜中的纳米颗粒检测
DOI：
10.1021/acs.jpcb.3c00097
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Composto, Russell J.
中科院分区：
文献类型：
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作者：
Boyle, Michael J.;Goldman, Yale E.;Composto, Russell J.
干涉散射显微镜（iSCAT）是一种无标记的光学显微镜技术，可以对单个纳米物体（如纳米颗粒、病毒和蛋白质）进行成像。该技术的关键是抑制背景散射和识别来自纳米物体的信号。在具有高粗糙度的基底存在的情况下，背景中的散射异质性，再加上微小的舞台运动，导致背景中的特征在背景抑制的iSCAT图像中表现出来。传统的计算机视觉算法将这些背景特征作为粒子来检测，限制了iSCAT实验中目标检测的准确性。在这里，我们提出了一种途径，通过基于掩模区域的卷积神经网络（mask R-CNN）使用监督机器学习来改进这种情况下的粒子检测。利用19.2 nm的金纳米颗粒吸附在粗糙的多层聚电解质膜上的模型iSCAT实验，我们开发了一种利用实验背景图像和模拟粒子信号生成标记数据集的方法，并通过迁移学习在有限的计算资源下训练掩膜R-CNN。然后，我们通过分析模型实验数据，比较了在数据集中包含实验背景和不包含实验背景的掩模R-CNN与传统计算机视觉目标检测算法（Haar-like feature detection）的性能。结果表明，在训练数据集中加入代表性背景可以提高掩膜R-CNN区分背景和粒子信号的能力，并通过显著减少误报来提高性能。创建具有代表性实验背景和模拟信号的标记数据集的方法促进了机器学习在强背景散射的iSCAT实验中的应用，从而为未来的研究人员提高其图像处理能力提供了有用的工作流程。
Interferometric scattering microscopy (iSCAT) is a label-free optical microscopy technique that enables imaging of individual nano-objects such as nanoparticles, viruses, and proteins. Essential to this technique is the suppression of background scattering and identification of signals from nano-objects. In the presence of substrates with high roughness, scattering heterogeneities in the background, when coupled with tiny stage movements, cause features in the background to be manifested in background-suppressed iSCAT images. Traditional computer vision algorithms detect these background features as particles, limiting the accuracy of object detection in iSCAT experiments. Here, we present a pathway to improve particle detection in such situations using supervised machine learning via a mask region-based convolutional neural network (mask R-CNN). Using a model iSCAT experiment of 19.2 nm gold nanoparticles adsorbing to a rough layer-by-layer polyelectrolyte film, we develop a method to generate labeled datasets using experimental background images and simulated particle signals and train the mask R-CNN using limited computational resources via transfer learning. We then compare the performance of the mask R-CNN trained with and without inclusion of experimental backgrounds in the dataset against that of a traditional computer vision object detection algorithm, Haar-like feature detection, by analyzing data from the model experiment. Results demonstrate that including representative backgrounds in training datasets improved the mask R-CNN in differentiating between background and particle signals and elevated performance by markedly reducing false positives. The methodology for creating a labeled dataset with representative experimental backgrounds and simulated signals facilitates the application of machine learning in iSCAT experiments with strong background scattering and thus provides a useful workflow for future researchers to improve their image processing capabilities.