论文标题
通过弱监督的学习和图形优化整个Embryo C.秀丽隐杆线谱系的跟踪
Tracking by weakly-supervised learning and graph optimization for whole-embryo C. elegans lineages
论文作者
论文摘要
在嘈杂和致密的荧光显微镜数据中跟踪胚胎的所有核是一项具有挑战性的任务。我们建立在最新的核跟踪方法的基础上，该方法结合了从一小部分核心中心点注释与整数线性程序（ILP）结合了弱监督的学习，以实现最佳的细胞谱系提取。我们的工作专门解决了秀丽隐杆线虫胚胎记录的以下具有挑战性的特性：（1）与其他生物的基准记录相比，许多细胞分裂以及（2）很容易被误认为是细胞核的极性体。为了应对（1），我们设计并纳入了学习的细胞分裂检测器。为了应付（2），我们采用了学习的极性身体检测器。我们进一步提出了通过结构化的SVM进行自动化的ILP权重调整，从而减轻了对各自的网格搜索进行乏味的手动设置的需求。我们的方法的表现优于Fluo-N3DH-CE胚胎数据集上细胞跟踪挑战的先前领导者。我们报告了另外两个秀丽隐杆线虫数据集的进一步广泛的定量评估。我们将公开这些数据集作为未来方法开发的扩展基准。我们的结果表明，我们的方法产生了很大的改进，尤其是在分区事件检测的正确性以及完全正确的轨道段的数量和长度方面。代码：https：//github.com/funkelab/linajea
Tracking all nuclei of an embryo in noisy and dense fluorescence microscopy data is a challenging task. We build upon a recent method for nuclei tracking that combines weakly-supervised learning from a small set of nuclei center point annotations with an integer linear program (ILP) for optimal cell lineage extraction. Our work specifically addresses the following challenging properties of C. elegans embryo recordings: (1) Many cell divisions as compared to benchmark recordings of other organisms, and (2) the presence of polar bodies that are easily mistaken as cell nuclei. To cope with (1), we devise and incorporate a learnt cell division detector. To cope with (2), we employ a learnt polar body detector. We further propose automated ILP weights tuning via a structured SVM, alleviating the need for tedious manual set-up of a respective grid search. Our method outperforms the previous leader of the cell tracking challenge on the Fluo-N3DH-CE embryo dataset. We report a further extensive quantitative evaluation on two more C. elegans datasets. We will make these datasets public to serve as an extended benchmark for future method development. Our results suggest considerable improvements yielded by our method, especially in terms of the correctness of division event detection and the number and length of fully correct track segments. Code: https://github.com/funkelab/linajea
