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首页> 外文期刊>European Radiology >Quantification of left ventricular function and mass in cardiac Dual-Source CT (DSCT) exams: comparison of manual and semiautomatic segmentation algorithms
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Quantification of left ventricular function and mass in cardiac Dual-Source CT (DSCT) exams: comparison of manual and semiautomatic segmentation algorithms

机译:心脏双源CT(DSCT)检查中左心室功能和肿块的量化:手动和半自动分割算法的比较

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摘要

The purpose of our study was to evaluate reliability of left ventricular (LV) function and mass quantification in cardiac DSCT exams comparing manual contour tracing and a region-growing-based semiautomatic segmentation analysis software. Thirty-three consecutive patients who underwent cardiac DSCT exams were included. Axial 1-mm slices were used for the semiautomated technique, and short-axis 8-mm slice thickness multiphase image reconstructions were the basis for manual contour tracing. Left ventricular volumes, ejection fraction and myocardial mass were assessed by both segmentation methods. Length of time needed for both techniques was also recorded. Left ventricular functional parameters derived from semiautomatic contour detection algorithm were not statistically different from manual tracing and showed an excellent correlation (p<0.001). The semiautomatic contour detection algorithm overestimated LV mass (180.30±44.74 g) compared with manual contour tracing (156.07±46.29 g) (p<0.001). This software allowed a significant reduction of the time needed for global LV assessment (mean 174.16±71.53 s, p<0.001). Objective quantification of LV function using the evaluated region-growing-based semiautomatic segmentation analysis software is feasible, accurate, reliable and time-effective. However, further improvements are needed to equal results achieved by manual contour tracing, especially with regard to LV mass quantification.
机译:我们研究的目的是评估心脏DSCT检查中左心室(LV)功能和质量定量的可靠性,比较手动轮廓跟踪和基于区域增长的半自动分割分析软件。包括连续接受心脏DSCT检查的33例患者。轴向1毫米切片用于半自动技术,而短轴8毫米切片厚度多相图像重建是手动轮廓跟踪的基础。通过两种分割方法评估左心室容积,射血分数和心肌质量。还记录了两种技术所需的时间长度。从半自动轮廓检测算法得出的左心室功能参数与手动追踪没有统计学差异,并显示出极好的相关性(p <0.001)。与手动轮廓追踪(156.07±46.29 g)相比,半自动轮廓检测算法高估了LV质量(180.30±44.74 g)(p <0.001)。该软件可以显着减少总体LV评估所需的时间(平均174.16±71.53 s,p <0.001)。使用基于评估的基于区域增长的半自动分割分析软件对LV功能进行客观量化是可行,准确,可靠且省时的。但是,需要进一步的改进以使通过手动轮廓跟踪获得的结果相等,特别是在LV质量定量方面。

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