Robust plant cell tracking using local spatio-temporal context
Robust plant cell tracking using local spatio-temporal context
复制标题
DOI：
10.1016/j.neucom.2015.12.124
复制
发表时间：
2016-10
期刊：
影响因子：
6
通讯作者：
Min Liu;Peng Xiang;Guocai Liu
中科院分区：
文献类型：
--
作者：
Min Liu;Peng Xiang;Guocai Liu
在活跃发育的植物组织中自动分割和跟踪细胞可以提供一系列细胞行为的高通量和定量时空测量。提出了一种基于细胞时空背景信息的茎尖分生组织细胞自动分割和跟踪方法。该方法利用细胞的空间背景信息，比其他局部图匹配方法具有更好的鲁棒性。跟踪输出作为分割质量的一个指标，反过来，过分割和欠分割的错误被纠正的局部调整方法，这被证明是更准确和更有效的比全局校正方法。此外，通过使用细胞的时空背景信息来关联细胞的谱系轨迹，以获得长期谱系。我们在两个数据集上的结果验证了所提出的方法的有效性，我们能够在长期时间内跟踪99%的植物细胞。
Automated segmentation and tracking of cells in actively developing plant tissues can provide high-throughput and quantitative spatio-temporal measurements of a range of cell behaviors. In this paper, we propose an automated segmentation and tracking method for the shoot apical meristem cells based on the cells׳ spatio-temporal contextual information. The cells are properly segmented and then tracked by using the proposed Triangle Neighborhood Structure matching method, which exploits the cells’ spatial context and turns out to be more robust than the other local graph matching methods. The tracking output acts as an indicator of the quality of segmentation and, in turn, the over-segmentation and under-segmentation errors are corrected by a local adjustment method, which is proved to be much more accurate and efficient than the global correction method. Furthermore, the cells׳ lineage tracklets are associated by using the cells׳ spatio–temporal contextual information to obtain long-term lineages. Our results on two datasets validate the effectiveness of the proposed method and we are able to track 99% of the plant cells across a long-term time period.