Abstract

Motivation: Cell tracking is an important method to quantitatively analyze time-lapse microscopy data. While numerous methods and tools exist for tracking cells in two-dimensional (2D) time-lapse images, only few and very application specific tracking tools are available for three-dimensional (3D) time-lapse images, which is of high relevance in immunoimaging, in particular for studying the motility of microglia in vivo. Results: We introduce a novel algorithm for tracking cells in 3D time-lapse microscopy data, based on computing cosegmentations between component trees representing individual time frames using so-called tree-assignments. For the first time, our method allows to track microglia in three dimensional confocal time-lapse microscopy images.We also evaluate our method on synthetically generated data, demonstrating that our algorithm is robust even in the presence of different types of inhomogeneous background noise. Availability: Our algorithm is implemented in the ct3d package, which is available under