School of Information Science and Technology, Nantong University, Nantong, Jiangsu Province, China.
Adv Exp Med Biol. 2024;1463:257-262. doi: 10.1007/978-3-031-67458-7_43.
Carotid artery (CA) stenosis (CAS) constitutes a significant factor to ischaemic cerebrovascular events which exhibiting no overt symptoms in the early stages. Early detection of CAS can prevent ischaemic stroke and improve patient prognosis. In this study, we developed a non-invasive CAS automatic assessment method based on deep learning, intended for the early detection of CAS with CT imaging. The method proposed in this paper consists of three main components. First, we utilised thresholding and the Hessian-based Frangi filter to eliminate irrelevant tissue and enhance vascular structures. Second, we introduced a novel neural network named parameter shared axial attention (PSAA)-nnUNet for the automatic segmentation of CA. Finally, we assessed the degree of CAS with the North American Symptomatic Carotid Endarterectomy Trial (NASCET) formula. The PSAA-nnUNet algorithm proposed in this study achieved a segmentation accuracy of 0.82. The non-invasive CAS automatic assessment method based on PSAA-nnUNet exhibits excellent accuracy and great application potential.
颈动脉（CA）狭窄（CAS）是缺血性脑血管事件的重要因素，在早期阶段没有明显症状。早期发现 CAS 可以预防缺血性中风并改善患者预后。在这项研究中，我们开发了一种基于深度学习的无创 CAS 自动评估方法，用于 CT 成像的早期 CAS 检测。本文提出的方法主要由三个部分组成。首先，我们利用阈值处理和基于 Hessian 的 Frangi 滤波器来消除无关组织并增强血管结构。其次，我们引入了一种名为参数共享轴向注意力（PSAA）-nnUNet 的新型神经网络，用于 CA 的自动分割。最后，我们使用北美症状性颈动脉内膜切除术试验（NASCET）公式评估 CAS 的程度。本文提出的 PSAA-nnUNet 算法的分割准确率为 0.82。基于 PSAA-nnUNet 的无创 CAS 自动评估方法具有出色的准确性和巨大的应用潜力。
