论文标题
在3D中跟踪Janus Microswimmers在机器学习的情况下
Tracking Janus microswimmers in 3D with Machine Learning
论文作者
论文摘要
人工积极物质的进步在很大程度上取决于我们表征其运动的能力。然而，使用最广泛的分析后者的工具是标准的宽场显微镜，这在很大程度上限于研究二维运动。相比之下，现实世界中的应用通常需要导航复杂的三维环境。在这里，我们提出了一种机器学习（ML）方法，以使用Z-stacks作为标记的培训数据来跟踪Janus Microswimmers的三个维度。我们使用可自由使用且有据可查的软件展示了ML算法的几个示例，并发现基于决策树的模型（极端随机的决策树）在跟踪粒子的深度超过40 $μ$ m的体积上表现最佳。通过此模型，我们能够将Janus颗粒与标准宽视野显微镜图像具有显着的光学不对称性定位，从而绕开了对专业设备和专业知识的需求，例如数字全息显微镜所需的需求。我们预计，在活跃物质系统的研究中，ML算法将变得越来越普遍，并鼓励实验者利用这种强大的工具来应对该领域的各种挑战。
Advancements in artificial active matter heavily rely on our ability to characterise their motion. Yet, the most widely used tool to analyse the latter is standard wide-field microscopy, which is largely limited to the study of two-dimensional motion. In contrast, real-world applications often require the navigation of complex three-dimensional environments. Here, we present a Machine Learning (ML) approach to track Janus microswimmers in three dimensions, using Z-stacks as labelled training data. We demonstrate several examples of ML algorithms using freely available and well-documented software, and find that an ensemble decision tree-based model (Extremely Randomised Decision Trees) performs the best at tracking the particles over a volume spanning a depth of more than 40 $μ$m. With this model, we are able to localise Janus particles with a significant optical asymmetry from standard wide-field microscopy images, bypassing the need for specialised equipment and expertise such as that required for digital holographic microscopy. We expect that ML algorithms will become increasingly prevalent by necessity in the study of active matter systems, and encourage experimentalists to take advantage of this powerful tool to address the various challenges within the field.
