Segmentation of Aortic Vessel Tree in CT Scans with Deep Fully
  Convolutional Networks
Shaofeng Yuan Feng Yang
Automatic and accurate segmentation of aortic vessel tree (AVT) in computed
tomography (CT) scans is crucial for early detection, diagnosis and prognosis
of aortic diseases, such as aneurysms, dissections and stenosis. However, this
task remains challenges, due to the complexity of aortic vessel tree and amount
of CT angiography data. In this technical report, we use two-stage fully
convolutional networks (FCNs) to automatically segment AVT in CTA scans from
multiple centers. Specifically, we firstly adopt a 3D FCN with U-shape network
architecture to segment AVT in order to produce topology attention and
accelerate medical image analysis pipeline. And then another one 3D FCN is
trained to segment branches of AVT along the pseudo-centerline of AVT. In the
2023 MICCAI Segmentation of the Aorta (SEG.A.) Challenge , the reported method
was evaluated on the public dataset of 56 cases. The resulting Dice Similarity
Coefficient (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 training
and validation set.