面向细菌生物被膜密集三维显微视频的深度时序分类与数学建模细胞追踪方法
Deep Temporal Sequence Classification and Mathematical Modeling for Cell Tracking in Dense 3D Microscopy Videos of Bacterial Biofilms
AI总结：
本文提出DenseTrack算法，将细胞追踪建模为深度时序分类加约束一对一匹配优化问题，并结合特征分解检测细胞分裂，在密集细菌生物被膜三维视频中显著优于现有方法。
摘要：
密集环境中的自动细胞追踪长期受到对应关系不准确和亲代-子代关系误识别的困扰。本文提出了一种名为DenseTrack的新型细胞追踪算法，该算法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并检测细胞分裂事件。我们将细胞追踪问题形式化为一个基于深度学习的时序分类任务，随后利用分类器的置信度分数求解一个受约束的一对一匹配优化问题。此外，我们提出了一种基于特征分解的细胞分裂检测策略，该策略利用了细胞几何形状的先验知识。通过在细菌生物被膜发育的三维时间序列图像中对密集堆积的细胞进行追踪，评估了所提方法的性能。在模拟和实验荧光图像序列上的实验结果表明，与近期最先进的细胞追踪方法相比，所提出的追踪方法在定性和定量评估指标上均取得了优越的性能。
英文摘要：
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.
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