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Abstract
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.