Department of Ultrasound, Second Affiliated Hospital of Harbin Medical University, Harbin, PR China.
Ultrasound Med Biol. 2012 Jan;38(1):119-27. doi: 10.1016/j.ultrasmedbio.2011.09.011. Epub 2011 Nov 21.
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.
我们研究了使用一种新的分割算法对超声影像中良恶性肿块进行区分时对放射科医生的敏感度和特异度的影响。我们对 510 张常规超声图像进行了新的分割算法处理。邀请了 5 名放射科医生独立分析原始图像和计算机处理后的图像。通过接收者操作特性（ROC）曲线分析评估有或没有计算机辅助的放射科医生的表现。分割算法处理后，肿块变得更加明显。在不使用算法的情况下，5 名放射科医生的 ROC 曲线下面积（Az）范围为 0.70∼0.84。使用算法后，Az 显著增加（范围，0.79∼0.88；p < 0.001）。该分割算法通过减少图像斑点并提取肿块边界特征，提高了放射科医生的诊断性能。
