Data from: Trajectory energy minimisation for cell growth tracking and genealogy analysis

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.利用微流控装置开展的细胞生长实验可生成大规模延时图像数据集，其中蕴含细胞生长及其谱系模式的关键信息。为提取此类有效信息，本文提出一种可自动完成细菌细胞分割与追踪的方案。相较于多数将分割与追踪拆分为两个独立流程的已发表方法，我们聚焦于设计一种算法，通过将单帧的分割与追踪结果传递至下一帧，以刻画连续帧间细胞属性的演化过程。该方案通过最小化距离正则化水平集演化（distance regularized level set evolution, DRLSE）模型提取细胞边界。通过识别细胞隔膜与细胞膜，并针对延时图像序列构建轨迹能量最小化函数，实现对单个细菌细胞的识别与追踪。实验结果表明，应用本方案可自动测量细胞的生长与分裂过程；在不同数据集上开展测试并与其他现有算法对比后，验证了本方法的有效性。所提方案在大规模细菌细胞生长分析领域展现出巨大应用潜力。