Data_Sheet_9_Motion history images: a new method for tracking microswimmers in 3D.zip

Quantitative tracking of rapidly moving micron-scale objects remains an elusive challenge in microscopy due to low signal-to-noise. This paper describes a novel method for tracking micron-sized motile organisms in off-axis Digital Holographic Microscope (DHM) raw holograms and/or reconstructions. We begin by processing the microscopic images with the previously reported Holographic Examination for Life-like Motility (HELM) software, which provides a variety of tracking outputs including motion history images (MHIs). MHIs are stills of videos where the frame-to-frame changes are indicated with color time-coding. This exposes tracks of objects that are difficult to identify in individual frames at a low signal-to-noise ratio. The visible tracks in the MHIs are superior to tracks identified by all tested automated tracking algorithms that start from object identification at the frame level, particularly in low signal-to-noise ratio data, but do not provide quantitative track data. In contrast to other tracking methods, like Kalman filter, where the recording is analyzed frame by frame, MHIs show the whole time span of particle movement at once and eliminate the need to identify objects in individual frames. This feature also enables post-tracking identification of low-SNR objects. We use these tracks, rather than object identification in individual frames, as a basis for quantitative tracking of Bacillus subtilis by first generating MHIs from X, Y, and t stacks (raw holograms or a projection over reconstructed planes), then using a region-tracking algorithm to identify and separate swimming pathways. Subsequently, we identify each object's Z plane of best focus at the corresponding X, Y, and t points, yielding ap full description of the swimming pathways in three spatial dimensions plus time. This approach offers an alternative to object-based tracking for processing large, low signal-to-noise datasets containing highly motile organisms.

在显微镜领域，对快速移动的微米级物体进行定量追踪仍然是一项充满挑战的任务，这主要归因于信号与噪声比的低劣。本文介绍了一种新颖的方法，用于在离轴数字全息显微镜（DHM）的原始全息图及/或重建图像中追踪微米级可动生物。我们首先利用先前报道的用于生命体运动类似性的全息检查软件（Helm）对显微图像进行处理，该软件提供多种追踪输出，包括运动历史图像（MHIs）。MHIs 是视频的静态图像，其中帧与帧之间的变化通过彩色时间编码来指示。这揭示了在低信号与噪声比下难以在单个帧中识别的物体轨迹。在 MHIs 中可见的轨迹优于所有测试的从帧级物体识别开始的自动追踪算法识别的轨迹，尤其是在低信号与噪声比的数据中，但这些算法并不提供定量轨迹数据。与像卡尔曼滤波这样的其他追踪方法不同，后者是逐帧分析记录，MHIs 一次展示了整个粒子运动的时间跨度，并消除了在单个帧中识别物体的必要性。这一特性还使得对低信噪比物体的追踪后识别成为可能。我们使用这些轨迹，而不是单个帧中的物体识别，作为定量追踪枯草芽孢杆菌的基础，首先从 X、Y 和 t 堆栈（原始全息图或重建平面的投影）生成 MHIs，然后使用区域追踪算法识别和分离游泳路径。随后，我们在相应的 X、Y 和 t 点识别每个物体的最佳聚焦 Z 平面，从而得到三维空间加上时间的三维游泳路径的完整描述。这种方法为处理包含高度可动生物的大规模、低信号与噪声比数据集提供了基于物体追踪的替代方案。