A Centerline-Guided Approach for Aorta and Stent-Graft Segmentation

Monitoring of patients after Endovascular aortic repair (EVAR) is a clinical necessity due to the high re-intervention rate associated with the treatment. The risk assessment could be greatly enhanced by the inclusion of metrics based on the aortic blood-flow and stent-graft changes. A preliminary step to this endeavour is, however, the automatic reconstruction of the relevant structures: aortic bloodlumen and the stent-graft wire frame. In this paper we present a centerline-guided approach that leverages knowledge about the target structures through a combination of two 3D U-Nets for efficient automated segmentation of both structures. We evaluate our approach on a real-world clinical dataset yielding Dice similarity coefficients of 0.942 and 0.841 for the blood lumen and stent-graft metal wire, respectively.