Cao Qing, Broersen Alexander, de Graaf Michiel A, Kitslaar Pieter H, Yang Guanyu, Scholte Arthur J, Lelieveldt Boudewijn P F, Reiber Johan H C, Dijkstra Jouke
Division of Image Processing, Department of Radiology, C2S, Leiden University Medical Center, PO Box 9600, Albinusdreef 2, 2300 RC, Leiden, The Netherlands.
Department of Cardiology, Leiden University Medical Center, Leiden, The Netherlands.
Int J Cardiovasc Imaging. 2017 Nov;33(11):1809-1819. doi: 10.1007/s10554-017-1169-0. Epub 2017 Jun 24.
An automatic coronary artery tree labeling algorithm is described to identify the anatomical segments of the extracted centerlines from coronary computed tomography angiography (CCTA) images. This method will facilitate the automatic lesion reporting and risk stratification of cardiovascular disease. Three-dimensional (3D) models for both right dominant (RD) and left dominant (LD) coronary circulations were built. All labels in the model were matched with their possible candidates in the extracted tree to find the optimal labeling result. In total, 83 CCTA datasets with 1149 segments were included in the testing of the algorithm. The results of the automatic labeling were compared with those by two experts. In all cases, the proximal parts of main branches including LM were labeled correctly. The automatic labeling algorithm was able to identify and assign labels to 89.2% RD and 83.6% LD coronary tree segments in comparison with the agreements of the two experts (97.6% RD, 87.6% LD). The average precision of start and end points of segments was 92.0% for RD and 90.7% for LD in comparison with the manual identification by two experts while average differences in experts is 1.0% in RD and 2.2% in LD cases. All cases got similar clinical risk scores as the two experts. The presented fully automatic labeling algorithm can identify and assign labels to the extracted coronary centerlines for both RD and LD circulations.
描述了一种自动冠状动脉树标记算法，用于从冠状动脉计算机断层扫描血管造影（CCTA）图像中识别提取中心线的解剖段。该方法将有助于心血管疾病的自动病变报告和风险分层。构建了右优势（RD）和左优势（LD）冠状动脉循环的三维（3D）模型。将模型中的所有标签与其在提取树中的可能候选标签进行匹配，以找到最佳标记结果。算法测试共纳入83个CCTA数据集，包含1149个段。将自动标记结果与两位专家的标记结果进行比较。在所有情况下，包括左主干（LM）在内的主要分支近端均被正确标记。与两位专家的一致性结果（RD为97.6%，LD为87.6%）相比，自动标记算法能够识别并为89.2%的RD和83.6%的LD冠状动脉树段分配标签。与两位专家手动识别相比，段起点和终点的平均精度在RD中为92.0%，在LD中为90.7%，而专家之间的平均差异在RD中为1.0%，在LD中为2.2%。所有病例的临床风险评分与两位专家的结果相似。所提出的全自动标记算法能够为RD和LD循环提取的冠状动脉中心线识别并分配标签。
