Wang Wenji, Xia Qing, Yan Zhennan, Hu Zhiqiang, Chen Yinan, Zheng Wen, Wang Xiao, Nie Shaoping, Metaxas Dimitris, Zhang Shaoting
SenseTime Research, Beijing, 100080, China.
SenseBrain Technology, NJ, 08540, USA.
Med Image Anal. 2024 Jan;91:102999. doi: 10.1016/j.media.2023.102999. Epub 2023 Oct 14.
Coronary CT angiography (CCTA) is an effective and non-invasive method for coronary artery disease diagnosis. Extracting an accurate coronary artery tree from CCTA image is essential for centerline extraction, plaque detection, and stenosis quantification. In practice, data quality varies. Sometimes, the arteries and veins have similar intensities and locate closely, which may confuse segmentation algorithms, even deep learning based ones, to obtain accurate arteries. However, it is not always feasible to re-scan the patient for better image quality. In this paper, we propose an artery and vein disentanglement network (AVDNet) for robust and accurate segmentation by incorporating the coronary vein into the segmentation task. This is the first work to segment coronary artery and vein at the same time. The AVDNet consists of an image based vessel recognition network (IVRN) and a topology based vessel refinement network (TVRN). IVRN learns to segment the arteries and veins, while TVRN learns to correct the segmentation errors based on topology consistency. We also design a novel inverse distance weighted dice (IDD) loss function to recover more thin vessel branches and preserve the vascular boundaries. Extensive experiments are conducted on a multi-center dataset of 700 patients. Quantitative and qualitative results demonstrate the effectiveness of the proposed method by comparing it with state-of-the-art methods and different variants. Prediction results of the AVDNet on the Automated Segmentation of Coronary Artery Challenge dataset are avaliabel at https://github.com/WennyJJ/Coronary-Artery-Vein-Segmentation for follow-up research.
冠状动脉CT血管造影（CCTA）是诊断冠状动脉疾病的一种有效且无创的方法。从CCTA图像中提取准确的冠状动脉树对于中心线提取、斑块检测和狭窄量化至关重要。在实际应用中，数据质量各不相同。有时，动脉和静脉具有相似的强度且位置相近，这可能会使分割算法（甚至是基于深度学习的算法）难以获得准确的动脉。然而，重新扫描患者以获得更好的图像质量并不总是可行的。在本文中，我们提出了一种动脉和静脉解缠网络（AVDNet），通过将冠状静脉纳入分割任务来进行稳健且准确的分割。这是首次同时分割冠状动脉和静脉的工作。AVDNet由基于图像的血管识别网络（IVRN）和基于拓扑的血管细化网络（TVRN）组成。IVRN学习分割动脉和静脉，而TVRN学习基于拓扑一致性校正分割错误。我们还设计了一种新颖的逆距离加权骰子（IDD）损失函数，以恢复更多细小的血管分支并保留血管边界。在一个包含700名患者的多中心数据集上进行了广泛的实验。通过与现有最先进的方法和不同变体进行比较，定量和定性结果证明了所提方法的有效性。AVDNet在冠状动脉自动分割挑战赛数据集上的预测结果可在https://github.com/WennyJJ/Coronary-Artery-Vein-Segmentation获取，以供后续研究使用。
