Effect of a novel segmentation algorithm on radiologists' diagnosis of breast masses using ultrasound imaging

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.