A Fully Automated Method for Segmenting Arteries and Quantifying Vessel Radii on Magnetic Resonance Angiography Images of Varying Projection Thickness
A Fully Automated Method for Segmenting Arteries and Quantifying Vessel Radii on Magnetic Resonance Angiography Images of Varying Projection Thickness
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DOI：
10.3389/fnins.2020.00537
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发表时间：
2020-06-16
影响因子：
4.3
通讯作者：
Lupo, Janine M.
中科院分区：
文献类型：
--
作者：
Avadiappan, Sivakami;Payabvash, Seyedmehdi;Lupo, Janine M.
目的脑动脉的精确定量有助于脑血管疾病的鉴别诊断和预后判断。现有的磁共振血管成像(MRA)图像处理和分割算法仅限于分析2D最大强度投影图像或整个3D体积。这项研究的目标是开发一种全自动的2D-3D混合方法，用于稳健地分割动脉，并使用不同投影厚度的MRA准确量化血管半径。方法提出一种基于自适应Frangi滤波的血管分割和血管半径估计的新算法。该方法在三个健康受试者的MRA数据集和相应的手动分割上进行了评估，以获得不同的投影厚度。此外，还计算了另外四个受试者的血管指标。在不同的噪声水平下，还对三个合成的类似脑血管的血管造影数据集进行了评估。使用骰子相似系数、Jaccard指数、F-Score和一致性相关系数来衡量人工分割和自动分割的分割精度。结果与已有方法相比，新的自适应滤波方法能够准确地表示血管，保持准确的血管半径，并且在不同投影厚度的情况下能更好地与人工分割相对应。在低对比度和噪声条件下用合成数据集进行验证，可以准确地量化血管，而不会失真。结论我们已经展示了一种自动分割血管树并随后生成血管半径图的方法。这项新技术可用于分析健康和疾病人群的动脉结构，并改善血管完整性的特征。
Purpose Precise quantification of cerebral arteries can help with differentiation and prognostication of cerebrovascular disease. Existing image processing and segmentation algorithms for magnetic resonance angiography (MRA) are limited to the analysis of either 2D maximum intensity projection images or the entire 3D volume. The goal of this study was to develop a fully automated, hybrid 2D-3D method for robust segmentation of arteries and accurate quantification of vessel radii using MRA at varying projection thicknesses. Methods A novel algorithm that employs an adaptive Frangi filter for segmentation of vessels followed by estimation of vessel radii is presented. The method was evaluated on MRA datasets and corresponding manual segmentations from three healthy subjects for various projection thicknesses. In addition, the vessel metrics were computed in four additional subjects. Three synthetically generated angiographic datasets resembling brain vasculature were also evaluated under different noise levels. Dice similarity coefficient, Jaccard Index, F-score, and concordance correlation coefficient were used to measure the segmentation accuracy of manual versus automatic segmentation. Results Our new adaptive filter rendered accurate representations of vessels, maintained accurate vessel radii, and corresponded better to manual segmentation at different projection thicknesses than prior methods. Validation with synthetic datasets under low contrast and noisy conditions revealed accurate quantification of vessels without distortions. Conclusion We have demonstrated a method for automatic segmentation of vascular trees and the subsequent generation of a vessel radii map. This novel technique can be applied to analyze arterial structures in healthy and diseased populations and improve the characterization of vascular integrity.