SPF-CellTracker: Tracking multiple cells with strongly-correlated moves using a spatial particle filter

TL;DR

SPF-CellTracker is a novel spatial particle filter-based method for accurately tracking hundreds of cells in 3D time-lapse images by modeling their correlated movements to reduce tracking errors.

Contribution

The paper introduces a new multi-cell tracking method that models cell movement dependencies with a Markov random field and implements a fast spatial particle filter algorithm.

Findings

Outperforms standard particle filter in cell tracking accuracy.

Effectively handles strongly correlated cell movements.

Demonstrated on live C. elegans neuron data with ~120 nuclei.

Abstract

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 based only on shape and size, (iii) the number of imaged cells ranges in several hundreds, (iv) moves of nearly-located cells are strongly correlated and (v) cells do not divide. We developed a tracking software suite which we call SPF-CellTracker. Incorporating dependency on cells' moves into prediction model is the key to reduce the tracking errors: cell-switching and coalescence of tracked positions. We model target cells' correlated moves 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 nuclei of C. elegans neurons in which approximately 120 nuclei of neurons are imaged, we demonstrate an advantage of the proposed method over the standard particle filter and a method developed by Tokunaga et al. (2014).