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

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.

鉴于近年来在科学和技术领域的日益重要性，视频中的粒子追踪已成为成功获取生命科学和物理学新知识的重要工具。因此，针对不同场景开发了多种粒子追踪方法。在本文中，我们介绍了一种基于Python实现的粒子追踪应用，尤其适用于球形磁性粒子，包括超顺磁性微珠和Janus粒子。在后续内容中，我们将粒子追踪分为两个子步骤，即单帧中粒子的定位以及将后续帧中提取的粒子位置链接成轨迹。我们提供了一种基于强度的定位技术来检测粒子，以及两种链接算法，分别采用逐帧链接或线性分配问题求解。此外，我们还提供了自动预处理图像的有用工具，并通过利用机器学习估算定位算法所需的参数。作为额外功能，我们实现了一种估算Janus粒子在x-y平面内当前空间取向的技术。我们的框架易于扩展和使用，因为我们提供了图形用户界面和命令行工具。多种输出选项，如数据帧和视频，确保了进一步的可自动化分析。