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.

近年来，随着视频粒子追踪技术在科技领域的重要性与日俱增，其已然成为生命科学与物理领域获取前沿研究成果的关键工具。据此，针对不同应用场景的各类粒子追踪方法相继被提出。
本文提出一款基于Python开发的粒子追踪应用程序，可专门适配包括超顺磁微珠（superparamagnetic beads）与Janus粒子（Janus particles）在内的球形磁性颗粒的追踪需求。下文我们将粒子追踪流程划分为两个核心子步骤：一是单帧图像内的粒子定位，二是将后续帧中提取的粒子位置关联整合为运动轨迹。
本应用提供一种基于强度特征的粒子定位技术以实现颗粒检测，同时配备两种关联算法：分别采用逐帧关联策略与线性分配问题（linear assignment problem）求解方案。此外，本框架还集成了自动化图像预处理工具，并可通过机器学习方法估算定位算法所需的各类参数。
额外配套功能方面，我们还开发了一种可估算Janus粒子在x-y平面内当前空间姿态的算法。本框架兼具优异的可扩展性与易用性，同时提供图形用户界面（graphical user interface）与命令行工具两种交互入口。支持数据帧与视频等多种输出形式，可满足自动化后续分析的研究需求。