Widely accessible method for 3D microflow mapping at high spatial and temporal resolutions.

ABSTRACT: Advances in microfluidic technologies rely on engineered 3D flow patterns to manipulate samples at the microscale. However, current methods for mapping flows only provide limited 3D and temporal resolutions or require highly specialized optical set-ups. Here, we present a simple defocusing approach based on brightfield microscopy and open-source software to map micro-flows in 3D at high spatial and temporal resolution. Our workflow is both integrated in ImageJ and modular. We track seed particles in 2D before classifying their Z-position using a reference library. We compare the performance of a traditional cross-correlation method and a deep learning model in performing the classification step. We validate our method on three highly relevant microfluidic examples: a channel step expansion

SUBMITTER: Lammertse E 

PROVIDER: S-EPMC9246883 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature