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一种用于短轴二维超声心动图图像的基于计算机的第二代边缘检测算法:准确性及观察者间变异性的改善

A second-generation computer-based edge detection algorithm for short-axis, two-dimensional echocardiographic images: accuracy and improvement in interobserver variability.

作者信息

Geiser E A, Conetta D A, Limacher M C, Stockton V O, Oliver L H, Jones B

机构信息

University of Florida, Gainesville.

出版信息

J Am Soc Echocardiogr. 1990 Mar-Apr;3(2):79-90. doi: 10.1016/s0894-7317(14)80500-2.

Abstract

The present study tested the hypothesis that a second-generation endocardial edge detection algorithm that used a priori endocardial and epicardial information would improve accuracy and reduce the variability of border definition. Five nonexpert observers utilized the version 2 algorithm on 20 cycles of two-dimensional short-axis images (five excellent, seven good, and eight poor quality studies stored digitally from a previously reported project). Manually defined areas by five recognized experts on these 20 cardiac cycles were considered to be "true areas." Areas defined by the experts with version 1 of the algorithm were also used for comparison. Regression of the version 2 areas with mean, manually defined excellent quality areas yielded a similar correlation (r = 0.985) to that reported between the manual and the version 1 areas (r = 0.986). For all 20 cycles in the series, however, the correlation between version 2 and the manually defined areas was lower (r = 0.952) than that of the same correlation with version 1 areas (r = 0.980). For all studies the interobserver variability (percent area difference) was +/- 14.4% for manually defined borders, +/- 11.1% for version 1-defined borders, and +/- 7.7% for version 2-defined borders. No difference in variability was observed for excellent quality studies (+/- 5.3% versus 5.2%) between version 1 and version 2 areas. However, the version 2 algorithm significantly reduced interobserver variability for good and poor quality studies (+/- 8.4% to 7.6%, p less than 0.025, and 16.3% to 9.1%, p less than 0.05, respectively). We concluded that: the version 2 algorithm provided accuracy and significantly reduced the variability of area measurement in good and poor quality studies and that epicardial information was important to the improvement by providing wall thickness information to assist in filling areas of dropout and avoidance of intracavitary structures.

摘要

本研究检验了这样一个假设,即使用先验心内膜和心外膜信息的第二代心内膜边缘检测算法将提高准确性并降低边界定义的变异性。五名非专业观察者在20个心动周期的二维短轴图像上使用了版本2算法(这些图像来自之前报告项目中以数字方式存储的五项优质、七项良好和八项质量较差的研究)。由五名公认的专家在这20个心动周期上手动定义的区域被视为“真实区域”。专家使用算法版本1定义的区域也用于比较。版本2区域与平均手动定义的优质区域的回归产生的相关性(r = 0.985)与手动区域和版本1区域之间报告的相关性(r = 0.986)相似。然而,对于该系列中的所有20个心动周期,版本2与手动定义区域之间的相关性(r = 0.952)低于与版本1区域的相同相关性(r = 0.980)。对于所有研究,手动定义边界的观察者间变异性(面积差异百分比)为±14.4%,版本1定义边界为±11.1%,版本2定义边界为±7.7%。对于优质研究(±5.3%对5.2%),版本1和版本2区域之间未观察到变异性差异。然而,版本2算法显著降低了质量良好和较差研究的观察者间变异性(分别为±8.4%至7.6%,p小于0.025;以及16.3%至9.1%,p小于0.05)。我们得出结论:版本2算法在质量良好和较差的研究中提供了准确性并显著降低了面积测量的变异性,并且心外膜信息对于改进很重要,因为它提供了壁厚度信息以帮助填充缺失区域并避免腔内结构。

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