An ASP Approach for Arteries Classification in CT-scans

Automated segmentation of CT scans is the first step in the pipeline for the interpretation and identification of potential pathologies in human organs. Several methods based on Machine Learning are currently available, even if their precision is still outperformed by medical doctors. In this field there are some intrinsic limitations to ML approaches, such as the cost and time to acquire high quality annotated scans for training; a considerably high variability of organs morphology due to age, health conditions, genetics; acquisition noise. This paper 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.

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