The primary objective of this thesis is to investigate the possibilities of machine-learning-free methods for segmenting curvilinear structures in volumetric bioimage data and to develop a reliable image analysis pipeline for segmenting and quantifying tunneling nanotubes in multidimensional fluorescence microscopy image data of Drosophila's wings. The textual part of the thesis must include a concise and comprehensive description of the developed pipeline and its quantitative evaluation focused on segmentation accuracy, execution time, and memory consumption. The implementation of the developed pipeline will be delivered in an easy-to-use form and complemented with a practical guide that documents the exemplary usage of the developed pipeline.