Lareyre Fabien, Adam Cédric, Carrier Marion, Raffort Juliette
Department of Vascular Surgery, Hospital of Antibes Juan-les-Pins, 06600 Antibes, France.
Université Côte d'Azur, Inserm U1065, C3M, 06204 Nice, France.
J Clin Med. 2021 Jul 29;10(15):3347. doi: 10.3390/jcm10153347.
Computed tomography angiography (CTA) is one of the most commonly used imaging technique for the management of vascular diseases. Here, we aimed to develop a hybrid method combining a feature-based expert system with a supervised deep learning (DL) algorithm to enable a fully automatic segmentation of the abdominal vascular tree.
We proposed an algorithm based on the hybridization of a data-driven convolutional neural network and a knowledge-based model dedicated to vascular system segmentation. By using two distinct datasets of CTA from patients to evaluate independence to training dataset, the accuracy of the hybrid method for lumen and thrombus segmentation was evaluated compared to the feature-based expert system alone and to the ground truth provided by a human expert.
The hybrid approach demonstrated a better accuracy for lumen segmentation compared to the expert system alone (volume similarity: 0.8128 vs. 0.7912, = 0.0006 and Dice similarity coefficient: 0.8266 vs. 0.7942, < 0.0001). The accuracy for thrombus segmentation was also enhanced using the hybrid approach (volume similarity: 0.9404 vs. 0.9185, = 0.0027 and Dice similarity coefficient: 0.8918 vs. 0.8654, < 0.0001).
By enabling a robust and fully automatic segmentation, the method could be used to develop real-time decision support to help in the management of vascular diseases.
计算机断层血管造影（CTA）是血管疾病管理中最常用的成像技术之一。在此，我们旨在开发一种将基于特征的专家系统与监督深度学习（DL）算法相结合的混合方法，以实现腹部血管树的全自动分割。
我们提出了一种基于数据驱动的卷积神经网络与专用于血管系统分割的基于知识的模型相融合的算法。通过使用来自患者的两个不同的CTA数据集来评估对训练数据集的独立性，将混合方法在管腔和血栓分割方面的准确性与单独的基于特征的专家系统以及人类专家提供的真实情况进行了比较。
与单独的专家系统相比，混合方法在管腔分割方面表现出更高的准确性（体积相似度：0.8128对0.7912，P = 0.0006；骰子相似度系数：0.8266对0.7942，P < 0.0001）。使用混合方法血栓分割的准确性也得到了提高（体积相似度：0.9404对0.9185，P = 0.0027；骰子相似度系数：0.8918对0.8654，P < 0.0001）。
通过实现强大的全自动分割，该方法可用于开发实时决策支持，以帮助管理血管疾病。
