Spilger Roman, Imle Andrea, Lee Ji-Young, Muller Barbara, Fackler Oliver T, Bartenschlager Ralf, Rohr Karl
IEEE Trans Image Process. 2020 Jan 13. doi: 10.1109/TIP.2020.2964515.
Automatic tracking of particles in time-lapse fluorescence microscopy images is essential for quantifying the dynamic behavior of subcellular structures and virus structures. We introduce a novel particle tracking approach based on a deep recurrent neural network architecture that exploits past and future information in both forward and backward direction. Assignment probabilities are determined jointly across multiple detections, and the probability of missing detections is computed. In addition, existence probabilities are determined by the network to handle track initiation and termination. For correspondence finding, track hypotheses are propagated to future time points so that information at later time points can be used to resolve ambiguities. A handcrafted similarity measure and handcrafted motion features are not necessary. Manually labeled data is not required for network training. We evaluated the performance of our approach using image data of the Particle Tracking Challenge as well as real fluorescence microscopy image sequences of virus structures. It turned out that the proposed approach outperforms previous methods.
在延时荧光显微镜图像中自动跟踪粒子对于量化亚细胞结构和病毒结构的动态行为至关重要。我们引入了一种基于深度循环神经网络架构的新型粒子跟踪方法，该方法在向前和向后方向上利用过去和未来的信息。跨多个检测联合确定分配概率，并计算漏检概率。此外，网络确定存在概率以处理轨迹起始和终止。为了找到对应关系，将轨迹假设传播到未来时间点，以便可以使用稍后时间点的信息来解决模糊性。无需手工制作的相似性度量和手工制作的运动特征。网络训练不需要人工标记的数据。我们使用粒子跟踪挑战赛的图像数据以及病毒结构的真实荧光显微镜图像序列评估了我们方法的性能。结果表明，所提出的方法优于先前的方法。
