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

TL;DR

This paper presents a two-stage deep learning approach using 3D fully convolutional networks to automatically segment the aortic vessel tree in CT scans, achieving high accuracy in a public challenge dataset.

Contribution

The novel two-stage 3D FCN method improves AVT segmentation accuracy and efficiency in multi-center CT data, with topology attention and branch segmentation along pseudo-centerlines.

Findings

Dice Similarity Coefficient of 0.920

Jaccard Similarity Coefficient of 0.861

Recall of 0.922

Abstract

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.