A graph-based cell tracking algorithm with few manually tunable parameters and automated segmentation error correction
Fig 2
(a) shows a graph constructed from an image sequence with erroneous segmentation. Each segmented object is assigned an unique ID i. Nodes corresponding to a segmented object share the same ID i, however, depending on the node type these nodes are assigned to different time points t in the graph. We link segmented objects over a maximum time span of Δt = 2 frames by adding for each object node oi,t a skip node xi,t+1, which models a missing segmentation mask. The segmented objects are assigned to tracks by finding optimal paths—highlighted in black—through the graph. (b) visualizes how cell behavior and segmentation errors are modeled in the graph example (a). Annotations c(⋅, ⋅) on the edges are assigned edge costs. To model mitosis, edges which are connected to pairs of “daughter” nodes are pairwise coupled—highlighted in green.