Blood vessels provide oxygen and nutrients to all tissues in the human body, and their incorrect organisation or dysfunction contributes to several diseases. Correct organisation of blood vessels is achieved through vascular patterning, a process that relies on endothelial cell polarization and migration against the blood flow direction. Unravelling the mechanisms governing endothelial cell polarity is essential to study the process of vascular patterning. Cell polarity is defined by a vector that goes from the nucleus centroid to the corresponding Golgi complex centroid, here defined as axial polarity. Currently, axial polarity is calculated manually, which is time-consuming and subjective. In this work, we used a deep learning approach to segment nuclei and Golgi in 3D fluorescence microscopy images of mouse retinas, and to assign nucleus-Golgi pairs. This approach predicts nuclei and Golgi segmentation masks but also a third mask corresponding to joint nuclei and Golgi segmentations. The joint segmentation mask is used to perform nucleus-Golgi pairing. We demonstrate that our deep learning approach using three masks successfully identifies nucleus-Golgi pairs, outperforming a pairing method based on a cost matrix. Our results pave the way for automated computation of axial polarity in 3D tissues and in vivo.