The diagnostic performance of leak-plugging automated segmentation versus manual tracing of breast lesions on ultrasound images.

Purpose: To assess the diagnostic performance of a leak-plugging segmentation method that we have developed for delineating breast masses on ultrasound images.

Materials And Methods: Fifty-two biopsy-proven breast lesion images were analyzed by three observers using the leak-plugging and manual segmentation methods. From each segmentation method, grayscale and morphological features were extracted and classified as malignant or benign by logistic regression analysis.

Going beyond a First Reader: A Machine Learning Methodology for Optimizing Cost and Performance in Breast Ultrasound Diagnosis.

The goal of this study was to devise a machine learning methodology as a viable low-cost alternative to a second reader to help augment physicians' interpretations of breast ultrasound images in differentiating benign and malignant masses. Two independent feature sets consisting of visual features based on a radiologist's interpretation of images and computer-extracted features when used as first and second readers and combined by adaptive boosting (AdaBoost) and a pruning classifier resulted in a very high level of diagnostic performance (area under the receiver operating characteristic curve = 0.98) at a cost of pruning a fraction (20%) of the cases for further evaluation by independent methods.

Observer Variability in BI-RADS Ultrasound Features and Its Influence on Computer-Aided Diagnosis of Breast Masses.

Objective: Computer classification of sonographic BI-RADS features can aid differentiation of the malignant and benign masses. However, the variability in the diagnosis due to the differences in the observed features between the observations is not known. The goal of this study is to measure the variation in sonographic features between multiple observations and determine the effect of features variation on computer-aided diagnosis of the breast masses.

Bayesian probability of malignancy with BI-RADS sonographic features.

Objectives: The purpose of this study was to develop a quantitative approach for combining individual American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) sonographic features of breast masses for assessing the overall probability of malignancy.

Methods: Sonograms of solid breast masses were analyzed by 2 observers blinded to patient age, mammographic features, and lesion pathologic findings. BI-RADS sonographic features were determined by using American College of Radiology criteria.