AdaPT: Adaptable Particle Tracking for spherical microparticles in lab on chip systems

Abstract

Due to its rising importance in science and technology in recent years, particle tracking in videos presents itself as a tool for successfully acquiring new knowledge in the field of life sciences and physics. Accordingly, different particle tracking methods for various scenarios have been developed. In this article, we present a particle tracking application implemented in Python for, in particular, spherical magnetic particles, including superparamagnetic beads and Janus particles. In the following, we distinguish between two sub-steps in particle tracking, namely the localization of particles in single images and the linking of the extracted particle positions of the subsequent frames into trajectories. We provide an intensity-based localization technique to detect particles and two linking algorithms, which apply either frame-by-frame linking or linear assignment problem solving. Beyond that, we offer helpful tools to preprocess images automatically as well as estimate parameters required for the localization algorithm by utilizing machine learning. As an extra, we have implemented a technique to estimate the current spatial orientation of Janus particles within the x–y-plane. Our framework is readily extendable and easy-to-use as we offer a graphical user interface and a command-line tool. Various output options, such as data frames and videos, ensure further analysis that can be automated. Program summaryProgram Title: AdaPTCPC Library link to program files:https://doi.org/10.17632/xxpnsbv3cs.1Developer’s repository link:https://git.ies.uni-kassel.de/adapt/adaptLicensing provisions: MPL-2.0Programming language: Python 3.6Supplementary material: We provide supplementary material to increase the traceability of the provided example. It consists of an exemplary input video, the corresponding annotated video with tracked particles, a data frame including the tracking information, and a plot displaying the trajectories.Nature of problem: Particle tracking in videos is an important tool for acquiring new knowledge in diverse fields. Several particle tracking methods have been developed for these diverse applications. The presented particle tracking software has been developed for the motion analysis of spherical or close to spherical magnetic particles. Up until now, no easily extensible automated particle tracking software for close to spherical microparticles and their current positioning status is available.Solution method: AdaPT is an extensible, easy-to-use microparticle tracking application developed explicitly for lab on chip applications but easily extensible to other applications and further functionalities. Currently implemented linking algorithms are a frame-by-frame linking approach as well as an approach solving linear assignment problems. In addition to many assistance possibilities for the user in the form of estimates of parameter values through machine learning, we offer the particular option to determine the orientation and rotation of spherical polymer particles with hemispherical metallic caps (Janus particles). The application can be used via console and graphical user interface.Additional comments including restrictions and unusual features: This software requires video data with spherical or close to spherical magnetic particles. It was tested on videos containing spherical superparamagnetic and magnetic Janus particles. Only mobile particles are detected; immobile particles are ignored by the software, reducing the amount of output data considerably. As a unique feature, the spatial orientation within the x–y-plane of Janus particles can be determined. The application has been tested on a variety of two-dimensional particle motion patterns. The latest version of AdaPT can be found here: https://git.ies.uni-kassel.de/adapt/adapt.