[论文解读] Automated Artery Localization and Vessel Wall Segmentation of Magnetic Resonance Vessel Wall Images using Tracklet Refinement and Polar Conversion

Li Chen, Jie Sun+7 more|arXiv (Cornell University)|Sep 4, 2019

Cerebrovascular and Carotid Artery Diseases参考文献 29被引用 4

一句话总结

本文提出了一种完全自动化的系统，用于在磁共振血管壁成像中进行动脉定位和血管壁分割，采用轨迹段优化进行中心线检测，并通过极坐标变换提升分割精度。在颈动脉（n=116）和腘动脉（n=5）数据集上评估，Dice分数达到0.824，显著优于传统方法（0.576）和标准卷积神经网络（0.747）。

ABSTRACT

Quantitative analysis of vessel wall structures by automated vessel wall segmentation provides useful imaging biomarkers in evaluating atherosclerotic lesions and plaque progression time-efficiently. To quantify vessel wall features, drawing lumen and outer wall contours of the artery of interest is required. To alleviate manual labor in contour drawing, some computer-assisted tools exist, but manual preprocessing steps, such as region of interest identification and boundary initialization are needed. In addition, the prior knowledge of the ring shape of vessel wall is not taken into consideration in designing the segmentation method. In this work, trained on manual vessel wall contours, a fully automated artery localization and vessel wall segmentation system is proposed. A tracklet refinement algorithm is used to robustly identify the centerlines of arteries of interest from a neural network localization architecture. Image patches are extracted from the centerlines and segmented in a polar coordinate system to use 3D information and to overcome problems such as contour discontinuity and interference from neighboring vessels. From a carotid artery dataset with 116 subjects (3406 slices) and a popliteal artery dataset with 5 subjects (289 slices), the proposed system is shown to robustly identify the artery of interest and segment the vessel wall. The proposed system demonstrates better performance on the carotid dataset with a Dice similarity coefficient of 0.824, compared with traditional vessel wall segmentation methods, Dice of 0.576, and traditional convolutional neural network approaches, Dice of 0.747. This vessel wall segmentation system will facilitate research on atherosclerosis and assist radiologists in image review.

研究动机与目标

• 消除血管壁分割中手动预处理步骤，如感兴趣区域选择和边界初始化。
• 利用血管壁固有的环形结构以提高分割的鲁棒性和准确性。
• 开发一种完全自动化的3D磁共振血管壁图像中动脉定位与壁分割流程。
• 通过整合3D空间上下文信息并克服轮廓不连续性，提升分割性能。
• 通过减少人工操作，支持定量动脉粥样硬化斑块分析并促进放射科工作流程。

提出的方法

• 基于神经网络的定位架构在3D磁共振血管壁图像中识别候选动脉中心线。
• 应用轨迹段优化技术连接并优化跨切片的中心线片段，提升连续性和准确性。
• 沿优化后的中心线提取图像块，并转换为极坐标系，以保持3D血管壁几何结构。
• 极坐标变换有助于更好地建模环形血管壁结构，并减少邻近血管的干扰。
• 分割网络处理极坐标变换后的图像块，以勾勒出血管腔和外壁边界。
• 通过极坐标表示整合3D信息，增强对轮廓不连续性和噪声的鲁棒性。

实验结果

研究问题

• RQ1 轨迹段优化能否提升3D磁共振血管壁图像中动脉中心线检测的准确性和连续性？
• RQ2 极坐标变换是否通过更好地建模环形解剖结构来提升血管壁分割性能？
• RQ3 完全自动化的系统能否在无需手动初始化的情况下超越传统方法和基于CNN的分割方法？
• RQ4 所提出方法在不同血管类型（如颈动脉和腘动脉）上的表现如何？
• RQ5 整合解剖学先验知识（环形结构）在多大程度上提升了分割的鲁棒性和Dice分数？

主要发现

• 所提系统在颈动脉数据集上实现了0.824的Dice相似系数，显著优于传统方法（0.576）。
• 该系统在相同数据集上也优于标准卷积神经网络方法，后者Dice得分为0.747。
• 系统在颈动脉（3406个切片，116名受试者）和腘动脉（289个切片，5名受试者）数据集上均表现出稳健性能。
• 极坐标变换有效缓解了轮廓不连续性和邻近血管干扰问题。
• 轨迹段优化成功提升了跨切片的中心线连续性，从而实现了准确的图像块提取用于分割。
• 该方法消除了对人工感兴趣区域选择和边界初始化的需求，实现了完全自动化。