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首页> 外文会议>Conference on Medical Imaging 2008: Computer-Aided Diagnosis; 20080219-21; San Diego,CA(US) >Automated segmentation and tracking of coronary arteries in ECG-gated cardiac CT scans
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Automated segmentation and tracking of coronary arteries in ECG-gated cardiac CT scans

机译：心电门控心脏CT扫描中的冠状动脉自动分割和跟踪

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Cardiac CT has been reported to be an effective means for clinical diagnosis of coronary artery plaque disease. We are investigating the feasibility of developing a computer-assisted image analysis (CAA) system to assist radiologist in detection of coronary artery plaque disease in ECG-gated cardiac CT scans. The heart region was first extracted using morphological operations and an adaptive EM thresholding method. Vascular structures in the heart volume were enhanced by 3D multi-scale filtering and analysis of the eigenvalues of Hessian matrices using a vessel enhancement response function specially designed for coronary arteries. The enhanced vascular structures were then segmented by an EM estimation method. Finally, our newly developed 3D rolling balloon vessel tracking method (RBVT) was used to track the segmented coronary arteries. Starting at two manually identified points located at the origins of left and right coronary artery (LCA and RCA), the RBVT method moved a sphere of adaptive diameter along the vessels, tracking the vessels and identifying its branches automatically to generate the left and right coronary arterial trees. Ten cardiac CT scans that contained various degrees of coronary artery diseases were used as test data set for our vessel segmentation and tracking method. Two experienced thoracic radiologists visually examined the computer tracked coronary arteries on a graphical interface to count untracked false-negative (FN) branches (segments). A total of 27 artery segments were identified to be FNs in the 10 cases, ranging from 0 to 6 FN segments in each case. No FN artery segment was found in 2 cases.
机译：据报道，心脏CT是临床诊断冠状动脉斑块疾病的有效手段。我们正在研究开发一种计算机辅助图像分析（CAA）系统以协助放射科医生在ECG门控心脏CT扫描中检测冠状动脉斑块疾病的可行性。首先使用形态学运算和自适应EM阈值化方法提取心脏区域。使用专门为冠状动脉设计的血管增强响应功能，通过3D多尺度过滤和对Hessian矩阵的特征值进行分析，增强了心脏体积中的血管结构。然后通过EM估计方法分割增强的血管结构。最后，我们新开发的3D滚动球囊血管跟踪方法（RBVT）用于跟踪分割的冠状动脉。 RBVT方法从位于左右冠状动脉起点（LCA和RCA）的两个手动识别点开始，沿血管移动一个适应直径的球体，跟踪血管并自动识别其分支以生成左右冠状动脉动脉树。十次包含不同程度冠状动脉疾病的心脏CT扫描被用作我们血管分割和跟踪方法的测试数据集。两位经验丰富的胸腔放射科医生在图形界面上目视检查了计算机追踪到的冠状动脉，以计算未追踪到的假阴性（FN）分支（段）。在这10例病例中，总共鉴定出27个动脉段为FN，每种情况下从0到6个FN段。 2例未发现FN动脉节段。

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