Dynamic (4D) live imaging of cellular processes provides a wealth of data. In order to gain biological insight, the observed processes need to be quantified and therefore subcellular structures in the image data to be tracked. This reconstruction task is a challenging task currently done semi-automatically with huge manual effort. In this project we aim at reducing the required human labour by using quantitative models of growth cone and filopodia geometry and dynamics for a more robust and consistent algorithmic identification of subcellular structures.