Chen Li, Sun Jie, Canton Gador, Balu Niranjan, Hippe Daniel S, Zhao Xihai, Li Rui, Hatsukami Thomas S, Hwang Jenq-Neng, Yuan Chun
Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, 98195, USA.
Department of Radiology, University of Washington, Seattle, WA, 98195, USA.
IEEE Access. 2020;8:217603-217614. doi: 10.1109/access.2020.3040616. Epub 2020 Nov 25.
Quantitative analysis of blood vessel wall structures is important to study atherosclerotic diseases and assess cardiovascular event risks. To achieve this, accurate identification of vessel luminal and outer wall contours is needed. Computer-assisted tools exist, but manual preprocessing steps, such as region of interest identification and/or boundary initialization, are still needed. In addition, prior knowledge of the ring shape of vessel walls has not been fully explored in designing segmentation methods. In this work, a fully automated artery localization and vessel wall segmentation system is proposed. A tracklet refinement algorithm was adapted to robustly identify the artery of interest from a neural network-based artery centerline identification architecture. Image patches were extracted from the centerlines and converted in a polar coordinate system for vessel wall segmentation. The segmentation method used 3D polar information and overcame problems such as contour discontinuity, complex vessel geometry, and interference from neighboring vessels. Verified by a large (>32000 images) carotid artery dataset collected from multiple sites, the proposed system was shown to better automatically segment the vessel wall than traditional vessel wall segmentation methods or standard convolutional neural network approaches. In addition, a segmentation uncertainty score was estimated to effectively identify slices likely to have errors and prompt manual confirmation of the segmentation. This robust vessel wall segmentation system has applications in different vascular beds and will facilitate vessel wall feature extraction and cardiovascular risk assessment.
血管壁结构的定量分析对于研究动脉粥样硬化疾病和评估心血管事件风险至关重要。要做到这一点，需要准确识别血管腔和外壁轮廓。虽然存在计算机辅助工具，但仍需要手动预处理步骤，如感兴趣区域识别和/或边界初始化。此外，在设计分割方法时，尚未充分探索血管壁环形形状的先验知识。在这项工作中，提出了一种全自动动脉定位和血管壁分割系统。采用了一种轨迹细化算法，从基于神经网络的动脉中心线识别架构中稳健地识别感兴趣的动脉。从中心线提取图像块并转换到极坐标系中进行血管壁分割。该分割方法使用三维极坐标信息，克服了轮廓不连续、血管几何形状复杂以及相邻血管干扰等问题。通过从多个地点收集的大型（>32000张图像）颈动脉数据集进行验证，结果表明，与传统的血管壁分割方法或标准卷积神经网络方法相比，所提出的系统能够更好地自动分割血管壁。此外，还估计了分割不确定性分数，以有效识别可能存在错误的切片并促使人工确认分割。这种稳健的血管壁分割系统在不同血管床中有应用，并将有助于血管壁特征提取和心血管风险评估。
