Department of Physics and Center for Soft Matter Research, New York University, New York, New York 10003, United States.
J Phys Chem B. 2020 Mar 5;124(9):1602-1610. doi: 10.1021/acs.jpcb.9b10463. Epub 2020 Feb 25.
In-line holographic microscopy provides an unparalleled wealth of information about the properties of colloidal dispersions. Analyzing one colloidal particle's hologram with the Lorenz-Mie theory of light scattering yields the particle's three-dimensional position with nanometer precision while simultaneously reporting its size and refractive index with part-per-thousand resolution. Analyzing a few thousand holograms in this way provides a comprehensive picture of the particles that make up a dispersion, even for complex multicomponent systems. All of this valuable information comes at the cost of three computationally expensive steps: (1) identifying and localizing features of interest within recorded holograms, (2) estimating each particle's properties based on characteristics of the associated features, and finally (3) optimizing those estimates through pixel-by-pixel fits to a generative model. Here, we demonstrate an end-to-end implementation that is based entirely on machine-learning techniques. Characterizing and Tracking Colloids Holographically (CATCH) with deep convolutional neural networks is fast enough for real-time applications and otherwise outperforms conventional analytical algorithms, particularly for heterogeneous and crowded samples. We demonstrate this system's capabilities with experiments on free-flowing and holographically trapped colloidal spheres.
在线全息显微镜为胶体分散体的特性提供了无与伦比的丰富信息。用光的洛伦兹-米理论分析一个胶体粒子的全息图，可以以纳米精度得出粒子的三维位置，同时以千分之一的分辨率报告其大小和折射率。以这种方式分析几千个全息图，可以全面了解构成分散体的粒子，即使对于复杂的多组分系统也是如此。所有这些有价值的信息都是以三个计算成本高昂的步骤为代价的：（1）在记录的全息图中识别和定位感兴趣的特征，（2）根据相关特征估计每个粒子的特性，最后（3）通过逐像素拟合到生成模型来优化这些估计。在这里，我们展示了一个完全基于机器学习技术的端到端实现。基于深度卷积神经网络的胶体质点全息分析与跟踪（CATCH）速度足够快，可用于实时应用，在异构和拥挤的样本方面表现优于传统的分析算法。我们通过对自由流动和全息捕获胶体球的实验证明了该系统的功能。
