Synchrotron X-ray microtomography ($\mu$CT) gives access to images with a micrometric resolution. In the context of vascular imaging, this allows the study of structural properties of arterial walls, even for small animals such as the mouse. However, the images available with $\mu$CT are non-usual, and there is no method specifically designed for their processing and analysis. This article describes a first pipeline dedicated to the segmentation of $\mu$CT images of mice aorta. This pipeline builds upon conventional image processing paradigms and more recent deep learning approaches, and tackles the issue of multiscale analysis of huge-sized, high-resolution data. It provides promising results, assessed by comparison with manual annotation of sampled data. This methodological framework is a step forwards to a finer analysis of the internal structure of the aortic walls, especially for understanding the consequences of ageing and/or disease (e.g. diabetes) on the vessels architecture.