Department of Molecular Biology and Genetics, Koç University, Rumelifeneri Yolu, Sariyer, 34450, Istanbul, Turkey; Center for Translational Research, Koç University, Rumelifeneri Yolu, Sariyer, 34450, Istanbul, Turkey.
Department of Molecular Biology and Genetics, Koç University, Rumelifeneri Yolu, Sariyer, 34450, Istanbul, Turkey.
Comput Biol Med. 2022 Nov;150:106193. doi: 10.1016/j.compbiomed.2022.106193. Epub 2022 Oct 13.
Tracking biological objects such as cells or subcellular components imaged with time-lapse microscopy enables us to understand the molecular principles about the dynamics of cell behaviors. However, automatic object detection, segmentation and extracting trajectories remain as a rate-limiting step due to intrinsic challenges of video processing. This paper presents an adaptive tracking algorithm (Adtari) that automatically finds the optimum search radius and cell linkages to determine trajectories in consecutive frames. A critical assumption in most tracking studies is that displacement remains unchanged throughout the movie and cells in a few frames are usually analyzed to determine its magnitude. Tracking errors and inaccurate association of cells may occur if the user does not correctly evaluate the value or prior knowledge is not present on cell movement. The key novelty of our method is that minimum intercellular distance and maximum displacement of cells between frames are dynamically computed and used to determine the threshold distance. Since the space between cells is highly variable in a given frame, our software recursively alters the magnitude to determine all plausible matches in the trajectory analysis. Our method therefore eliminates a major preprocessing step where a constant distance was used to determine the neighbor cells in tracking methods. Cells having multiple overlaps and splitting events were further evaluated by using the shape attributes including perimeter, area, ellipticity and distance. The features were applied to determine the closest matches by minimizing the difference in their magnitudes. Finally, reporting section of our software were used to generate instant maps by overlaying cell features and trajectories. Adtari was validated by using videos with variable signal-to-noise, contrast ratio and cell density. We compared the adaptive tracking with constant distance and other methods to evaluate performance and its efficiency. Our algorithm yields reduced mismatch ratio, increased ratio of whole cell track, higher frame tracking efficiency and allows layer-by-layer assessment of motility to characterize single-cells. Adaptive tracking provides a reliable, accurate, time efficient and user-friendly open source software that is well suited for analysis of 2D fluorescence microscopy video datasets.
跟踪通过延时显微镜成像的生物对象，如细胞或亚细胞成分，使我们能够了解关于细胞行为动力学的分子原理。然而，由于视频处理的内在挑战，自动对象检测、分割和提取轨迹仍然是一个限速步骤。本文提出了一种自适应跟踪算法 (Adtari)，它可以自动找到最佳搜索半径和细胞连接，以确定连续帧中的轨迹。在大多数跟踪研究中，一个关键的假设是位移在整个电影中保持不变，并且通常分析少数几帧中的细胞来确定其幅度。如果用户不正确评估值或没有细胞运动的先验知识，则可能会出现跟踪误差和细胞关联不准确的情况。我们方法的关键新颖之处在于，帧间细胞的最小细胞间距离和最大位移是动态计算的，并用于确定阈值距离。由于给定帧中细胞之间的空间高度可变，我们的软件递归地改变幅度以确定轨迹分析中的所有可能匹配。因此，我们的方法消除了跟踪方法中使用恒定距离来确定相邻细胞的主要预处理步骤。具有多个重叠和分裂事件的细胞进一步通过使用包括周长、面积、椭圆度和距离在内的形状属性进行评估。这些特征用于通过最小化它们幅度的差异来确定最接近的匹配。最后，我们的软件的报告部分用于通过覆盖细胞特征和轨迹生成即时地图。Adtari 通过使用具有可变信噪比、对比度比和细胞密度的视频进行了验证。我们将自适应跟踪与恒定距离和其他方法进行了比较，以评估性能和效率。我们的算法降低了不匹配率，提高了整个细胞轨迹的比例，提高了帧跟踪效率，并允许对运动性进行逐层评估，以表征单细胞。自适应跟踪提供了一种可靠、准确、高效且用户友好的开源软件，非常适合分析 2D 荧光显微镜视频数据集。
