Background and objective

Accurate coronary artery tree segmentation can now be developed to assist radiologists in detecting coronary artery disease. In clinical medicine, the noise, low contrast, and uneven intensity of medical images along with complex shapes and vessel bifurcation structures make coronary artery segmentation challenging. In this work, we propose a multiobjective clustering and toroidal model-guided tracking method that can accurately extract coronary arteries from computed tomography angiography (CTA) imagery.

Methods

Utilizing integrated noise reduction, candidate region detection, geometric feature extraction, and coronary artery tracking techniques, a new segmentation framework for 3D coronary artery trees is presented. The candidate regions are extracted using a multiobjective clustering method, and the coronary arteries are tracked by a toroidal model-guided tracking method.

Results

The qualitative and quantitative results demonstrate the effectiveness of the presented framework, which achieves better performance than the compared segmentation methods in three widely used evaluation indices: the Dice similarity coefficient (DSC), Jaccard index and Recall across the CTA data. The proposed method can accurately identify the coronary artery tree with a mean DSC of 84$\%,$ a Jaccard index of 74$\%,$ and a Recall of 93$\%$.

Conclusions

The proposed segmentation framework effectively segments the coronary tree from the CTA volume, which improves the accuracy of 3D vascular tree segmentation.

中文翻译：

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多目标聚类和环形模型引导跟踪方法在冠状动脉CTA中自动进行冠状动脉树分割

背景和目标

现在可以开发出准确的冠状动脉树分割技术，以协助放射科医生检测冠状动脉疾病。在临床医学中，噪声，低对比度，医学图像强度不均匀以及复杂的形状和血管分叉结构使冠状动脉分割具有挑战性。在这项工作中，我们提出了一种多目标聚类和环形模型指导的跟踪方法，该方法可以从计算机断层摄影血管造影（CTA）图像中准确提取冠状动脉。

方法

利用集成的降噪，候选区域检测，几何特征提取和冠状动脉跟踪技术，提出了一种用于3D冠状动脉树的新分割框架。使用多目标聚类方法提取候选区域，并通过环形模型引导的跟踪方法跟踪冠状动脉。

结果

定性和定量结果证明了所提出框架的有效性，该框架在三种广泛使用的评估指标（Dice相似系数（DSC），Jaccard指数和整个CTA数据的召回率）中比比较的细分方法具有更好的性能。所提出的方法可以准确地识别平均DSC为84的冠状动脉树$％，$ Jaccard索引为74$％，$ 并召回93$％$。

结论

提出的分割框架从CTA体积有效地分割了冠状动脉树，从而提高了3D血管树分割的准确性。