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Optimal Live Cell Tracking for Cell Cycle Study Using Time-Lapse Fluorescent Microscopy Images

机译:使用时间间隔荧光显微镜图像进行细胞周期研究的最佳直播电池跟踪

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Cell cycle study using time-lapse fluorescent microscopy images is important for understanding the mechanisms of cell division and screening of anti-cancer drugs. Cell tracking is necessary for quantifying cell behaviors. However, the complex behaviors and similarity of individual cells in a dense population make the cell population tracking challenging. To deal with these challenges, we propose a novel tracking algorithm, in which the local neighboring information is introduced to distinguish the nearby cells with similar morphology, and the Interacting Multiple Model (IMM) filter is employed to compensate for cell migrations. Based on a similarity metric, integrating the local neighboring information, migration prediction, shape and intensity, the integer programming is used to achieve the most stable association between cells in two consecutive frames. We evaluated the proposed method on the high content screening assays of HeLa cancer cell populations, and achieved 92% average tracking accuracy.
机译:使用时移荧光显微图像细胞周期的研究是理解细胞分裂的机制和抗癌药物筛选的重要。细胞追踪是必要的量化细胞行为。然而,复杂的行为,并在人口密集的单个细胞的相似性使细胞群跟踪挑战。为了应对这些挑战,我们提出了一个新的跟踪算法,其中地方周边信息介绍来区分附近的细胞形态相似,并且采用交互式多模型(IMM)滤波器,以补偿细胞迁移。基于相似性度量,集成本地邻近信息,迁移预测,形状和强度,整数规划用于实现在两个连续帧小区之间的最稳定的缔合。我们评估在高含量筛选的HeLa癌细胞群体的测定法所提出的方法,取得了92%的平均跟踪精度。

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