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An Automatic Method for Renal Cortex Segmentation on CT Images. Evaluation on Kidney Donors

机译:CT图像上肾皮质分割的自动方法。肾脏捐赠者评估

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Rationale and Objectives: The aims of this study were to develop and validate an automated method to segment the renal cortex on contrast-enhanced abdominal computed tomographic images from kidney donors and to track cortex volume change after donation. Materials and Methods: A three-dimensional fully automated renal cortex segmentation method was developed and validated on 37 arterial phase computed tomographic data sets (27 patients, 10 of whom underwent two computed tomographic scans before and after nephrectomy) using leave-one-out strategy. Two expert interpreters manually segmented the cortex slice by slice, and linear regression analysis and Bland-Altman plots were used to compare automated and manual segmentation. The true-positive and false-positive volume fractions were also calculated to evaluate the accuracy of the proposed method. Cortex volume changes in 10 subjects were also calculated. Results: The linear regression analysis results showed that the automated and manual segmentation methods had strong correlations, with Pearson's correlations of 0.9529, 0.9309, 0.9283, and 0.9124 between intraobserver variation, interobserver variation, automated and user 1, and automated and user 2, respectively (P < .001 for all analyses). The Bland-Altman plots for cortex segmentation also showed that the automated and manual methods had agreeable segmentation. The mean volume increase of the cortex for the 10 subjects was 35.1 ± 13.2% (P < .01 by paired t test). The overall true-positive and false-positive volume fractions for cortex segmentation were 90.15 ± 3.11% and 0.85 ± 0.05%. With the proposed automated method, the time for cortex segmentation was reduced from 20 minutes for manual segmentation to 2 minutes. Conclusions: The proposed method was accurate and efficient and can replace the current subjective and time-consuming manual procedure. The computer measurement confirms the volume of renal cortex increases after kidney donation.
机译:原理和目的:本研究的目的是开发和验证一种自动方法,该方法可在来自肾脏供体的对比增强的腹部计算机断层扫描图像上分割肾皮质,并追踪捐赠后的皮质体积变化。材料和方法:开发了一种三维全自动肾皮质分割方法,并使用留一法对37个动脉期计算机断层扫描数据集(27例患者,其中10例在肾切除术前后进行了两次计算机断层扫描)进行了验证。 。两名专业口译人员将皮层切片逐段手动分割,然后使用线性回归分析和Bland-Altman图来比较自动分割和手动分割。还计算了正负体积分数,以评估所提出方法的准确性。还计算了10位受试者的皮质体积变化。结果:线性回归分析结果表明,自动和手动分割方法之间具有很强的相关性,观察者内变异,观察者间变异,自动化和用户1以及自动化和用户2的Pearson关联分别为0.9529、0.9309、0.9283和0.9124。 (对于所有分析,P <0.001)。用于皮层分割的Bland-Altman图还显示自动和手动方法具有令人满意的分割效果。 10名受试者的皮层平均体积增加为35.1±13.2%(配对t检验,P <0.01)。皮层分割的总正,假阳性体积分数分别为90.15±3.11%和0.85±0.05%。使用建议的自动方法,将皮质分割的时间从手动分割的20分钟减少到2分钟。结论:所提出的方法准确有效,可以替代当前的主观且耗时的人工程序。电脑测量结果证实肾脏捐献后肾皮质的体积增加。

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