Automatic Segmentation of the Abdominal Aorta and Stent-Grafts

Understanding and monitoring changes of the treated vessel after Endovascular aneurysm repair is crucial for the prediction of complications and risk assessment to facilitate timely intervention. Due to the complexity of the stent-graft wire frame enveloping the aortic blood lumen and the inherent artifacts caused by the metal wire, segmenting the structures required for simulation and further analysis is a non-trivial task. In this paper we present a fully automatic segmentation architecture combining two 3D U-Nets in a novel patching approach leveraging knowledge of the target anatomy. We evaluated our approach on a real world clinical dataset against a competitive baseline, yielding results that surpass the baseline in both accuracy and computation time. On our data we achieve a median Dice similarity coefficient of 0.97 for the blood lumen and 0.88 for the stent-graft segmentation. We point out two common flaws in current segmentation models: undersampling and indiscriminate patching. By addressing them appropriately, our approach gains an advantage that may benefit a multitude of segmentation tasks.