  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).
