Accurate Coronary Centerline Extraction, Caliber Estimation, and Catheter Detection in Angiographies
Accurate Coronary Centerline Extraction, Caliber Estimation, and Catheter Detection in Angiographies
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DOI：
10.1109/titb.2012.2220781
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发表时间：
2012-11-01
影响因子：
--
通讯作者：
Radeva, Petia
中科院分区：
文献类型：
--
作者：
Hernandez-Vela, Antonio;Gatta, Carlo;Radeva, Petia
x线血管造影中冠状动脉的分割是评估动脉疾病和选择合适的冠状动脉治疗的基本工具。冠状动脉的准确分割已成为不同模式注册的重要课题，它使医生能够从计算机断层扫描（CT）或磁共振成像（MRI）中快速获取不同的医学成像信息。在本文中，我们提出了一种基于图切割的精确全自动算法，用于血管中心线提取，口径估计和导管检测。通过对Graph-cuts能量函数的全局优化，将容器性、测地线路径和一种新的多尺度边缘图结合起来，自定义Graph-cuts方法来分割管状结构。此外，提出了一种新的监督学习方法，该方法集成了局部和上下文信息，用于自动检测导管。我们在来自不同成像系统的三个数据集上评估了该方法的性能。在中心线检测和口径估计方面，该方法的性能与专家观测器相当。鉴别动脉与导管的准确度为96.5%，灵敏度为72%，精密度为97.4%。
Segmentation of coronary arteries in X-ray angiography is a fundamental tool to evaluate arterial diseases and choose proper coronary treatment. The accurate segmentation of coronary arteries has become an important topic for the registration of different modalities, which allows physicians rapid access to different medical imaging information from computed tomography (CT) scans or magnetic resonance imaging (MRI). In this paper, we propose an accurate fully automatic algorithm based on Graph-cuts for vessel centerline extraction, caliber estimation, and catheter detection. Vesselness, geodesic paths, and a new multiscale edgeness map are combined to customize the Graph-cuts approach to the segmentation of tubular structures, by means of a global optimization of the Graph-cuts energy function. Moreover, a novel supervised learning methodology that integrates local and contextual information is proposed for automatic catheter detection. We evaluate the method performance on three datasets coming from different imaging systems. The method performs as good as the expert observer with respect to centerline detection and caliber estimation. Moreover, the method discriminates between arteries and catheter with an accuracy of 96.5%, sensitivity of 72%, and precision of 97.4%.