Machine-learning techniques for fast and accurate holographic particle tracking

Quantitative analysis of holographic microscopy images yields the three-dimensional positions of micrometer-scale colloidal particles with nanometer precision, while simultaneously measuring the particles' sizes and refractive indexes. Extracting this information begins by detecting and localizing features of interest within individual holograms. Conventionally approached with heuristic algorithms, this image analysis problem can be solved faster and more generally with machine-learning techniques. We demonstrate that two popular machine-learning algorithms, cascade classifiers and deep convolutional neural networks (CNN), can solve the particle-tracking problem orders of magnitude faster than current state-of-the-art techniques. Our CNN implementation localizes holographic features precisely enough to bootstrap more detailed analyses based on the Lorenz-Mie theory of light scattering. The wavelet-based Haar cascade performs less well at detecting and localizing particles, but is so computationally efficient that it creates new opportunities for applications that emphasize speed and low cost. We demonstrate its use as a real-time targeting system for holographic optical trapping.