Automated segmentation of CT scans is the first step in the pipeline for the interpretation and identification of potential patholo- gies in human organs. Several methods based on Machine Learning are currently available, even if their precision is still outperformed by med- ical doctors. In this field there are some intrinsic limitations to ML ap- proaches, such as the cost and time to acquire high quality annotated scans for training; a considerably high variability of organs morphol- ogy due to age, health conditions, genetics; acquisition noise. This pa- per outlines a new methodology based on Answer Set Programming, which returns reliable, easy-to-program and explainable interpretations. In particular, we focus on the CT scan analysis and retrieval of tree-like structure, corresponding to main blood vessels (arteries) arrangement. The structure is compared to the knowledge base of vessels contained in anatomy text-books. The mapping of vessels names is computed by an ASP program. This preliminary step produces a robust input to a reasoner for the multi-organ labeling and localization problem.
