Dumay A C, Gerbrands J J, Reiber J H
University Hospital Leiden, Department of Diagnostic Radiology and Nuclear Medicine, The Netherlands.
Int J Card Imaging. 1994 Sep;10(3):205-15. doi: 10.1007/BF01137902.
For clinical decision-making and documentation purposes we have developed techniques to extract, label and analyze the coronary vasculature from arteriograms in an automated, quantitative manner. Advanced image processing techniques were applied to extract and analyze the vasculatures from non-subtracted arteriograms while artificial intelligence techniques were employed to assign anatomical labels. Lumen diameters of 11 phantom vessels were assessed with an accuracy of 0.27 +/- 0.19 mm (dtrue = 0.45 + 0.92dmeasured; r > 0.99) and 0.21 +/- 0.15 mm (dtrue = 0.42 + 0.91dmeasured; r > 0.99), from cine and digital images, respectively. We collected a total of 15 routinely acquired cine-arteriograms showing 74 vessel segments with 18 stenoses (severity larger than 30% assessed quantitatively), and 53 digital arteriograms showing 236 vessel segments with 69 stenoses. From the cine arteriograms we extracted 64 (86%) of the vessel segments without manual correction and 196 (83%) from the digital arteriograms. Repeated analysis (3 times) of the arteriograms by the same operator resulted in a standard deviation of the mean segment diameters (precision) of 0.064 mm for the cine-images and 0.020 mm for the digital images, while the standard deviations in the measurement of the minimal luminal diameter of the observed stenoses were 0.020 mm and 0.019 mm, respectively. The LAD artery, the septal and diagonal branches were correctly identified automatically in 86% of the segments. From these evaluations we conclude that our automated approach provides reliable tools for the assessment of multi-vessel disease, both in an off- and on-line environment.
为了临床决策和记录的目的，我们已经开发出技术，以自动、定量的方式从动脉造影中提取、标记和分析冠状动脉血管系统。先进的图像处理技术被用于从未减影的动脉造影中提取和分析血管系统，同时采用人工智能技术来分配解剖学标签。对11个模拟血管的管腔直径进行了评估，从电影图像和数字图像中得到的评估精度分别为0.27±0.19毫米（真实直径dtrue = 0.45 + 0.92×测量直径dmeasured；r > 0.99）和0.21±0.15毫米（真实直径dtrue = 0.42 + 0.91×测量直径dmeasured；r > 0.99）。我们总共收集了15份常规采集的电影动脉造影，显示74个血管节段，其中有18处狭窄（定量评估严重程度大于30%），以及53份数字动脉造影，显示236个血管节段，其中有69处狭窄。从电影动脉造影中，我们在无需人工校正的情况下提取了64个（86%）血管节段，从数字动脉造影中提取了196个（83%）血管节段。同一名操作人员对动脉造影进行重复分析（3次），电影图像的平均节段直径（精度）的标准偏差为0.064毫米，数字图像为0.020毫米，而观察到的狭窄处最小管腔直径测量的标准偏差分别为0.020毫米和0.019毫米。左前降支动脉、间隔支和对角支在86%的节段中被自动正确识别。从这些评估中我们得出结论，我们的自动化方法为在离线和在线环境中评估多支血管疾病提供了可靠的工具。
