Abstract
We introduce a novel segmentation method for time-lapse image stacks of neurites based on the co-segmentation principle. Our method aggregates information from multiple stacks to improve the segmentation task, using a neurite model and a tree similarity term. The neurite model takes into account branching characteristics, such as local shape smoothness and continuity, while the tree similarity term exploits the local branch dynamics across image stacks. Our approach improves accuracy in ambiguous regions, handling successfully out-of-focus effects and branching bifurcations. We validated our method using Drosophila sensory neuron datasets and made comparisons with existing methods.