SPF-CellTracker: Tracking Multiple Cells with Strongly-Correlated Moves Using a Spatial Particle Filter

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).

[1]  Michael I. Jordan,et al.  Revisiting k-means: New Algorithms via Bayesian Nonparametrics , 2011, ICML.

[2]  Frank Dellaert,et al.  An MCMC-Based Particle Filter for Tracking Multiple Interacting Targets , 2004, ECCV.

[3]  Yue Wang,et al.  Visualization and correction of automated segmentation, tracking and lineaging from 5-D stem cell image sequences , 2014, BMC Bioinformatics.

[4]  Erik Meijering,et al.  Methods for cell and particle tracking. , 2012, Methods in enzymology.

[5]  Carlos Ortiz-de-Solorzano,et al.  Segmentation and Shape Tracking of Whole Fluorescent Cells Based on the Chan–Vese Model , 2013, IEEE Transactions on Medical Imaging.

[6]  Wiro J. Niessen,et al.  Rao-Blackwellized Marginal Particle Filtering for Multiple Object Tracking in Molecular Bioimaging , 2007, IPMI.

[7]  Fernand Meyer,et al.  Graph-based object tracking , 2003, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).

[8]  Nathalie Harder,et al.  A benchmark for comparison of cell tracking algorithms , 2014, Bioinform..

[9]  Hwann-Tzong Chen,et al.  Multi-object tracking using dynamical graph matching , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[10]  Andrew Blake,et al.  Articulated body motion capture by annealed particle filtering , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[11]  Patrick Pérez,et al.  Maintaining multimodality through mixture tracking , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[12]  R. Butcher,et al.  Olfactory Plasticity Is Regulated by Pheromonal Signaling in Caenorhabditis elegans , 2010, Science.

[13]  Isabelle Bloch,et al.  Multiple Hypothesis Tracking for Cluttered Biological Image Sequences , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  William J. Godinez,et al.  Objective comparison of particle tracking methods , 2014, Nature Methods.

[15]  Xiaobo Zhou,et al.  Multiple Nuclei Tracking Using Integer Programming for Quantitative Cancer Cell Cycle Analysis , 2010, IEEE Transactions on Medical Imaging.

[16]  Yongxin Zhao,et al.  An Expanded Palette of Genetically Encoded Ca2+ Indicators , 2011, Science.

[17]  Wen Gao,et al.  Online selecting discriminative tracking features using particle filter , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[18]  Simon J. Godsill,et al.  On sequential Monte Carlo sampling methods for Bayesian filtering , 2000, Stat. Comput..

[19]  Kazushi Yoshida,et al.  Odour concentration-dependent olfactory preference change in C. elegans , 2012, Nature Communications.

[20]  Takayuki Teramoto,et al.  Automated detection and tracking of many cells by using 4D live-cell imaging data , 2014, Bioinform..

[21]  B. Roysam,et al.  Automated Cell Lineage Construction: A Rapid Method to Analyze Clonal Development Established with Murine Neural Progenitor Cells , 2006, Cell cycle.

[22]  Yong Rui,et al.  Better proposal distributions: object tracking using unscented particle filter , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[23]  Luc Van Gool,et al.  Object Tracking with an Adaptive Color-Based Particle Filter , 2002, DAGM-Symposium.

[24]  T. Başar,et al.  A New Approach to Linear Filtering and Prediction Problems , 2001 .

[25]  R. Prevedel,et al.  Brain-wide 3D imaging of neuronal activity in Caenorhabditis elegans with sculpted light , 2013, Nature Methods.

[26]  Damon A. Clark,et al.  Temporal Activity Patterns in Thermosensory Neurons of Freely Moving Caenorhabditis elegans Encode Spatial Thermal Gradients , 2007, The Journal of Neuroscience.

[27]  Jean-Christophe Olivo-Marin,et al.  3-D Active Meshes: Fast Discrete Deformable Models for Cell Tracking in 3-D Time-Lapse Microscopy , 2011, IEEE Transactions on Image Processing.

[28]  J. Kruskal.  On the shortest spanning subtree of a graph and the traveling salesman problem , 1956 .

[29]  Takeo Kanade,et al.  Cell population tracking and lineage construction with spatiotemporal context , 2008, Medical Image Anal..