Trajectory energy minimization for cell growth tracking and genealogy analysis.
Trajectory energy minimization for cell growth tracking and genealogy analysis.
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DOI：
10.1098/rsos.170207
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发表时间：
2017-05
影响因子：
3.5
通讯作者：
Tang HL
中科院分区：
文献类型：
--
作者：
Hu Y;Wang S;Ma N;Hingley-Wilson SM;Rocco A;McFadden J;Tang HL
使用微流体装置进行细胞生长实验会产生大规模延时图像数据，其中包含有关细胞生长及其谱系模式的重要信息。为了提取这些信息，我们提出了一种自动分割和跟踪细菌细胞的方案。与大多数已发表的方法（通常将分割和跟踪分成两个独立的过程）相反，我们专注于设计一种算法，通过将分割和跟踪结果从一帧馈送到下一帧来描述连续帧之间演变的细胞属性。通过最小化距离正则化水平集演化（DRLSE）模型来提取单元边界。通过识别细胞隔膜和细胞膜以及沿着延时序列开发轨迹能量最小化函数来识别和跟踪每个单独的细胞。实验表明，通过应用该方案，可以自动测量细胞的生长和分裂。结果显示了该方法在不同数据集上进行测试时的效率，并与其他现有算法进行了比较。所提出的方法展示了大规模细菌细胞生长分析的巨大潜力。
Cell growth experiments with a microfluidic device produce large-scale time-lapse image data, which contain important information on cell growth and patterns in their genealogy. To extract such information, we propose a scheme to segment and track bacterial cells automatically. In contrast with most published approaches, which often split segmentation and tracking into two independent procedures, we focus on designing an algorithm that describes cell properties evolving between consecutive frames by feeding segmentation and tracking results from one frame to the next one. The cell boundaries are extracted by minimizing the distance regularized level set evolution (DRLSE) model. Each individual cell was identified and tracked by identifying cell septum and membrane as well as developing a trajectory energy minimization function along time-lapse series. Experiments show that by applying this scheme, cell growth and division can be measured automatically. The results show the efficiency of the approach when testing on different datasets while comparing with other existing algorithms. The proposed approach demonstrates great potential for large-scale bacterial cell growth analysis.