@inproceedings{e72e4d14c71248e39e62e3e26bd5831b,
title = "Deep learning classification of particle depth for defocusing 3d-3c micro-PTV",
abstract = "We present a high-resolution micro-particle tracking velocimetry (uPTV) technique based on the defocused imaging of tracer particles via brightfield microscopy to experimentally map microfluidic flows. Our technique provides high-resolution 3D data with a regular optical set-up by shifting the technical burden to image processing. We use TrackMate, a free ImageJ plugin, to perform sub-pixel XY tracking, and we obtain Z data via classification of defocused particle images against a reference library of serially stepwise defocused images. Here, we compare a deep learning model with a traditional cross-correlation approach to perform classification.",
keywords = "Deep learning, Machine learning, Microfluidics, Particle tracking, PIV, PTV",
author = "Evan Lammertse and Martin Sauzade and Hongxiao Li and Jun Kong and Eric Brouzes",
note = "Publisher Copyright: {\textcopyright} 2020 CBMS-0001; 24th International Conference on Miniaturized Systems for Chemistry and Life Sciences, MicroTAS 2020 ; Conference date: 04-10-2020 Through 09-10-2020",
year = "2020",
language = "English",
series = "MicroTAS 2020 - 24th International Conference on Miniaturized Systems for Chemistry and Life Sciences",
publisher = "Chemical and Biological Microsystems Society",
pages = "1296--1297",
booktitle = "MicroTAS 2020 - 24th International Conference on Miniaturized Systems for Chemistry and Life Sciences",
}