Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, Liaoning, China.
School of Computer Science and Engineering, Northeastern University, Shenyang, Liaoning, China.
Int J Comput Assist Radiol Surg. 2022 Oct;17(10):1879-1890. doi: 10.1007/s11548-022-02655-7. Epub 2022 Jun 28.
Coronary artery segmentation in coronary computed tomography angiography (CTA) images plays a crucial role in diagnosing cardiovascular diseases. However, due to the complexity of coronary CTA images and coronary structure, it is difficult to automatically segment coronary arteries accurately and efficiently from numerous coronary CTA images.
In this study, an automatic method based on symmetrical radiation filter (SRF) and D-means is presented. The SRF, which is applied to the three orthogonal planes, is designed to filter the suspicious vessel tissue according to the features of gradient changes on vascular boundaries to segment coronary arteries accurately and reduce computational cost. Additionally, the D-means local clustering is proposed to be embedded into vessel segmentation to eliminate noise impact in coronary CTA images.
The results of the proposed method were compared against the manual delineations in 210 coronary CTA data sets. The average values of true positive, false positive, Jaccard measure, and Dice coefficient were [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text], respectively. Moreover, comparing the delineated data sets and public data sets showed that the proposed method is better than the related methods.
The experimental results indicate that the proposed method can perform complete, robust, and accurate segmentation of coronary arteries with low computational cost. Therefore, the proposed method is proven effective in vessel segmentation of coronary CTA images without extensive training data and can meet clinical applications.
冠状动脉 CT 血管造影（CTA）图像中的冠状动脉分割在心血管疾病的诊断中起着至关重要的作用。然而，由于冠状动脉 CTA 图像和冠状动脉结构的复杂性，很难从大量的冠状动脉 CTA 图像中自动准确高效地分割冠状动脉。
本研究提出了一种基于对称辐射滤波器（SRF）和 D-means 的自动方法。SRF 应用于三个正交平面，根据血管边界上梯度变化的特征设计，以过滤可疑的血管组织，从而准确分割冠状动脉，并降低计算成本。此外，还提出了 D-means 局部聚类嵌入到血管分割中，以消除冠状动脉 CTA 图像中的噪声影响。
将该方法的结果与 210 个冠状动脉 CTA 数据集的手动勾画结果进行了比较。真阳性、假阳性、Jaccard 度量和 Dice 系数的平均值分别为[公式：见文本]、[公式：见文本]、[公式：见文本]和[公式：见文本]。此外，与已勾画数据集和公共数据集的比较表明，该方法优于相关方法。
实验结果表明，该方法可以以较低的计算成本对冠状动脉进行完整、稳健和准确的分割。因此，该方法在无需广泛训练数据的情况下，可有效应用于冠状动脉 CTA 图像的血管分割，并能满足临床应用的需求。
