Lumen Segmentation of Aortic Dissection with Cascaded Convolutional Network

For the diagnosis and treatment of aortic dissection, where blood flows in between the layers of the aortic wall, the segmentation of true and false lumens is necessary. This is a challenging task because the intimal flap separating true and false lumens is thin, discontinuous and has a complex shape. In this paper we formulate lumen segmentation of aortic dissection as the extraction of aortic adventitia (the contour of aorta) and intima (the contour of true lumen). To this end, we propose a cascaded convolutional network for contour extraction on 2-D cross-section images, and then construct a 3-D adventitia and intima shape model. We performed a five-fold cross-validation on 45 aortic dissection CT volumes. The proposed method demonstrated a good performance for both aorta and true lumen segmentation, and the mean Dice similarity coefficient was 0.989 for aorta and 0.925 for true lumen.