Cardiac MRI makes it possible to examine the morphology of the heart and blood vessels. The 4D Flow MRI sequence is used to diagnose complex cardiovascular diseases with high accuracy. Segmentation of the pulmonary arteries and aorta is essential to quantify various hemodynamic parameters. However, manual segmentation of these images presents significant challenges due to factors such as low signal-to-noise ratio and phase accumulation errors. To address this challenge, we propose a semantic segmentation cascade for automatic segmentation of pulmonary artery branches and aortic sections obtained from 4D Flow MRI angiographic images. The obtained results show that our methodology achieves accurate and consistent segmentation in segmenting artery bifurcations and sections in a limited dataset, which underlines its potential applicability in clinical settings.