首页> 外文期刊>AJNR. American journal of neuroradiology >Automated cerebral infarct volume measurement in follow-up noncontrast CT scans of patients with acute ischemic stroke
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Automated cerebral infarct volume measurement in follow-up noncontrast CT scans of patients with acute ischemic stroke

机译:急性缺血性脑卒中患者的随访无造影CT扫描中自动进行脑梗死体积测量

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BACKGROUND AND PURPOSE: Cerebral infarct volume as observed in follow-up CT is an important radiologic outcome measure of the effectiveness of treatment of patients with acute ischemic stroke. However, manual measurement of CIV is time-consuming and operatordependent. The purpose of this study was to develop and evaluate a robust automated measurement of the CIV. MATERIALS AND METHODS: The CIV in early follow-up CT images of 34 consecutive patients with acute ischemic stroke was segmented with an automated intensity-based region-growing algorithm, which includes partial volume effect correction near the skull, midline determination, and ventricle and hemorrhage exclusion. Two observers manually delineated the CIV. Interobserver variability of the manual assessments and the accuracy of the automated method were evaluated by using the Pearson correlation, Bland-Altman analysis, and Dice coefficients. The accuracy was defined as the correlation with the manual assessment as a reference standard. RESULTS: The Pearson correlation for the automated method compared with the reference standard was similar to the manual correlation (R = 0.98). The accuracy of the automated method was excellent with a mean difference of 0.5 mL with limits of agreement of -38.0-39.1 mL, which were more consistent than the interobserver variability of the 2 observers (-40.9-44.1 mL). However, the Dice coefficients were higher for the manual delineation. CONCLUSIONS: The automated method showed a strong correlation and accuracy with the manual reference measurement. This approach has the potential to become the standard in assessing the infarct volume as a secondary outcome measure for evaluating the effectiveness of treatment.
机译:背景与目的:随访CT中观察到的脑梗死体积是一项重要的放射学结局指标,用于衡量急性缺血性卒中患者的治疗效果。但是,手动测量CIV既耗时又取决于操作员。这项研究的目的是开发和评估功能强大的CIV自动化测量。材料与方法:采用基于强度的自动区域增长算法对连续34例急性缺血性卒中患者的早期随访CT图像中的CIV进行了分割,该算法包括在颅骨附近进行部分体积效应校正,中线测定以及脑室和出血排除。两名观察员手动划定了CIV。人工评估的观察者间差异和自动化方法的准确性通过使用Pearson相关性,Bland-Altman分析和Dice系数进行了评估。准确度定义为与手动评估作为参考标准的相关性。结果:与参考标准相比,自动化方法的皮尔逊相关性与手动相关性相似(R = 0.98)。自动化方法的准确性非常好,平均差为0.5 mL,一致性极限为-38.0-39.1 mL,比2位观察者的观察者间差异(-40.9-44.1 mL)更一致。但是,对于手动划定,Dice系数较高。结论:自动化方法与手动参考测量显示出很强的相关性和准确性。这种方法有可能成为评估梗死面积的标准方法,作为评估治疗效果的次要结果。

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