The tracking-by-detection strategy is the backbone of many methods for tracking living cells in time-lapse microscopy. An object detector is first applied to the input images, and the resulting detection candidates are then linked by a data association module. The performance of such methods strongly depends on the quality of the detector because detection errors propagate to the linking step. To tackle this issue, we propose a joint model for segmentation, detection and tracking. The model is defined implicitly as limiting distribution of a Markov chain Monte Carlo algorithm and contains a temporal feedback, which allows to dynamically alter detector parameters using hints given by neighboring frames and, in this way, correct detection errors. The proposed method can integrate any detector and is therefore not restricted to a specific domain. The parameters of the model are learned using an objective based on empirical risk minimization. We use our method to conduct large-scale experiments for confluent cultures of endothelial cells and evaluate its performance in the ISBI Cell Tracking Challenge, where it consistently scored among the best three methods.

中文翻译：

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通过时间反馈耦合细胞检测和跟踪

通过检测跟踪策略是延时显微镜中跟踪活细胞的许多方法的基础。首先将对象检测器应用于输入图像，然后通过数据关联模块链接生成的检测候选对象。由于检测错误会传播到链接步骤，因此这些方法的性能在很大程度上取决于检测器的质量。为了解决这个问题，我们提出了一种用于分割，检测和跟踪的联合模型。该模型被隐式定义为马尔可夫链蒙特卡罗算法的极限分布，并包含一个时间反馈，该反馈允许使用相邻帧给出的提示动态更改检测器参数，并以此方式纠正检测错误。所提出的方法可以集成任何检测器，因此不限于特定领域。使用基于经验风险最小化的目标来学习模型的参数。我们使用我们的方法对内皮细胞的融合培养物进行大规模实验，并评估其在ISBI细胞追踪挑战赛中的表现，该挑战赛一直在最佳三种方法中名列前茅。