SPF-CellTracker: Tracking Multiple Cells with Strongly-Correlated Moves Using a Spatial Particle Filter
SPF-CellTracker: Tracking Multiple Cells with Strongly-Correlated Moves Using a Spatial Particle Filter
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DOI：
10.1109/tcbb.2017.2782255
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发表时间：
2018-11-01
影响因子：
4.5
通讯作者：
Yoshida, Ryo
中科院分区：
文献类型：
--
作者：
Hirose, Osamu;Kawaguchi, Shotaro;Yoshida, Ryo
实时三维生物图像序列中多细胞的跟踪是生物图像信息学中一项重要的挑战性任务。受C. elegans，我们提出了一种新的多细胞跟踪方法。该方法可应用的数据类型的特征如下：（i）细胞被成像为球状物体，（ii）仅基于形状和大小难以区分细胞，（iii）成像细胞的数量在几百个范围内，（iv）附近定位的细胞的运动强烈相关，以及（v）细胞不分裂。我们开发了一个跟踪软件套件，我们称之为SPF-CellTracker。将对细胞运动的依赖性纳入预测模型是减少跟踪误差的关键：细胞切换和跟踪位置的合并。我们将目标细胞的相关运动建模为马尔可夫随机场，并推导出一种快速计算算法，我们称之为空间粒子滤波。利用C. elegans神经元，其中大约120个神经元核被成像，所提出的方法与标准粒子滤波器和Tokunaga等人（2014）开发的方法相比证明了提高的准确性。
Tracking many cells in time-lapse 3D image sequences is an important challenging task of bioimage informatics. Motivated by a study of brain-wide 4D imaging of neural activity in C. elegans, we present a new method of multi-cell tracking. Data types to which the method is applicable are characterized as follows: (i) cells are imaged as globular-like objects, (ii) it is difficult to distinguish cells on the basis of shape and size only, (iii) the number of imaged cells in the several-hundred range, (iv) movements of nearly-located cells are strongly correlated, and (v) cells do not divide. We developed a tracking software suite that we call SPF-CellTracker. Incorporating dependency on the cells' movements into the prediction model is the key for reducing the tracking errors: the cell switching and the coalescence of the tracked positions. We model the target cells' correlated movements as a Markov random field and we also derive a fast computation algorithm, which we call spatial particle filter. With the live-imaging data of the nuclei of C. elegans neurons in which approximately 120 nuclei of neurons were imaged, the proposed method demonstrated improved accuracy compared to the standard particle filter and the method developed by Tokunaga et al. (2014).