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Effect of a Novel Segmentation Algorithm on Radiologists' Diagnosis of Breast Masses Using Ultrasound Imaging

机译:新型分割算法对放射科医师超声成像诊断乳腺肿块的影响

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摘要

We investigated the effect of using a novel segmentation algorithm on radiologists' sensitivity and specificity for discriminating malignant masses from benign masses using ultrasound. Five-hundred ten conventional ultrasound images were processed by a novel segmentation algorithm. Five radiologists were invited to analyze the original and computerized images independently. Performances of radiologists with or without computer aid were evaluated by receiver operating characteristic (ROC) curve analysis. The masses became more obvious after being processed by the segmentation algorithm. Without using the algorithm, the areas under the ROC curve (Az) of the five radiologists ranged from 0.70~0.84. Using the algorithm, the Az increased significantly (range, 0.79~0.88; p < 0.001). The proposed segmentation algorithm could improve the radiologists' diagnosis performance by reducing the image speckles and extracting the mass margin characteristics.
机译:我们调查了使用新颖的分割算法对放射科医生敏感性和特异性的影响,以利用超声将恶性肿块与良性肿块区分开。通过一种新颖的分割算法处理了五百十幅常规超声图像。邀请五位放射科医生独立分析原始图像和计算机图像。通过接收器工作特性(ROC)曲线分析评估了有或没有计算机辅助的放射科医生的表现。经过分割算法处理后,肿块变得更加明显。在不使用该算法的情况下,五位放射线医师的ROC曲线下面积(Az)在0.70〜0.84之间。使用该算法,Az显着增加(范围为0.79〜0.88; p <0.001)。所提出的分割算法可以通过减少图像斑点并提取质量余量特征来提高放射科医生的诊断性能。

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