全息术
跟踪(教育)
计算机科学
粒子(生态学)
像素
人工神经网络
光散射
球体
卷积神经网络
深度学习
人工智能
分辨率(逻辑)
职位(财务)
色散(光学)
胶体
光学
散射
生物系统
物理
化学
地质学
物理化学
经济
天文
海洋学
生物
教育学
心理学
财务
作者
Lauren E. Altman,David G. Grier
标识
DOI:10.1021/acs.jpcb.9b10463
摘要
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.
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