Multiple Hypothesis Tracking for Cluttered Biological Image Sequences
Multiple Hypothesis Tracking for Cluttered Biological Image Sequences
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DOI：
10.1109/tpami.2013.97
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发表时间：
2013-11-01
影响因子：
23.6
通讯作者：
Olivo-Marin, Jean-Christophe
中科院分区：
文献类型：
--
作者：
Chenouard, Nicolas;Bloch, Isabelle;Olivo-Marin, Jean-Christophe
在本文中，我们提出了一种在现代生物学中具有重要意义的生物图像序列中同时跟踪数千个目标的方法。由于问题的复杂性和固有的随机性，我们提出了一个统一的概率框架来跟踪显微镜图像中的生物粒子。该框架包括粒子运动和存在的现实模型以及荧光图像特征。对于轨迹提取过程本身而言，非常混乱的条件促使采用多帧方法，以强制跟踪决策鲁棒性，以适应较差的成像条件和随机目标运动。我们通过将多假设跟踪算法适应所提出的框架来解决问题的大规模性质，从而在模型复杂性和跟踪过程的计算成本之间取得了良好的权衡。当与最先进的生物成像跟踪技术相比，所提出的算法被证明是提供高质量结果的唯一方法，尽管成像条件非常差和密集的目标存在。因此，我们证明了先进的贝叶斯跟踪技术对动态生物过程的精确计算建模的好处，这对该领域的进一步发展是有希望的。
In this paper, we present a method for simultaneously tracking thousands of targets in biological image sequences, which is of major importance in modern biology. The complexity and inherent randomness of the problem lead us to propose a unified probabilistic framework for tracking biological particles in microscope images. The framework includes realistic models of particle motion and existence and of fluorescence image features. For the track extraction process per se, the very cluttered conditions motivate the adoption of a multiframe approach that enforces tracking decision robustness to poor imaging conditions and to random target movements. We tackle the large-scale nature of the problem by adapting the multiple hypothesis tracking algorithm to the proposed framework, resulting in a method with a favorable tradeoff between the model complexity and the computational cost of the tracking procedure. When compared to the state-of-the-art tracking techniques for bioimaging, the proposed algorithm is shown to be the only method providing high-quality results despite the critically poor imaging conditions and the dense target presence. We thus demonstrate the benefits of advanced Bayesian tracking techniques for the accurate computational modeling of dynamical biological processes, which is promising for further developments in this domain.