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Automatic and accurate segmentation of aortic vessel tree (AVT) in computedtomography (CT) scans is crucial for early detection, diagnosis and prognosisof aortic diseases, such as aneurysms, dissections and stenosis. However, thistask remains challenges, due to the complexity of aortic vessel tree and amountof CT angiography data. In this technical report, we use two-stage fullyconvolutional networks (FCNs) to automatically segment AVT in CTA scans frommultiple centers. Specifically, we firstly adopt a 3D FCN with U-shape networkarchitecture to segment AVT in order to produce topology attention andaccelerate medical image analysis pipeline. And then another one 3D FCN istrained to segment branches of AVT along the pseudo-centerline of AVT. In the2023 MICCAI Segmentation of the Aorta (SEG.A.) Challenge , the reported methodwas evaluated on the public dataset of 56 cases. The resulting Dice SimilarityCoefficient (DSC) is 0.920, Jaccard Similarity Coefficient (JSC) is 0.861,Recall is 0.922, and Precision is 0.926 on a 5-fold random split of trainingand validation set.

Segmentation of Aortic Vessel Tree in CT Scans with Deep Fully Convolutional Networks