Automatic cell tracking in dense environments is plagued by inaccurate
correspondences and misidentification of parent-offspring relationships. In
this paper, we introduce a novel cell tracking algorithm named DenseTrack,
which integrates deep learning with mathematical model-based strategies to
effectively establish correspondences between consecutive frames and detect
cell division events in crowded scenarios. We formulate the cell tracking
problem as a deep learning-based temporal sequence classification task followed
by solving a constrained one-to-one matching optimization problem exploiting
the classifier's confidence scores. Additionally, we present an
eigendecomposition-based cell division detection strategy that leverages
knowledge of cellular geometry. The performance of the proposed approach has
been evaluated by tracking densely packed cells in 3D time-lapse image
sequences of bacterial biofilm development. The experimental results on
simulated as well as experimental fluorescence image sequences suggest that the
proposed tracking method achieves superior performance in terms of both
qualitative and quantitative evaluation measures compared to recent
state-of-the-art cell tracking approaches.

本研究提出了一种名为 DenseTrack 的新型细胞跟踪算法，它将深度学习与基于数学模型的策略相结合，以在拥挤场景中有效建立连续帧之间的对应关系和检测细胞分裂事件。通过将细胞跟踪问题制定为基于深度学习的时间序列分类任务，然后通过解决约束的一对一匹配优化问题来利用分类器的置信度得分。此外，还提出了一种基于特征分解的细胞分裂检测策略，利用细胞几何知识。该方法在细菌生物膜发展的三维时间序列图像中跟踪密集堆积的细胞，并经过了模拟和实验荧光图像序列的实验证明，与最近的最先进细胞跟踪方法相比，所提出的跟踪方法在定性和定量评估指标上具有更优越的性能。