Breast lesion detection and characterization with 3D features

Automated Breast Ultrasound (ABUS) is highly effective as breast cancer screening adjunct technology. Automation can greatly enhance the efficiency of the clinician sifting through the quantum of data in ABUS volumes to spot lesions. We have implemented a fully automatic generic algorithm pipeline for detection and characterization of lesions on such 3D volumes. We compare a wide range of features for region description on their effectiveness at the dual goals of lesion detection and characterization. On multiple feature images, we compute region descriptors at lesion candidate locations obviating the need for explicit lesion segmentation. We use Random Forests classifier to evaluate candidate region descriptors for lesion detection. Further, we categorize true lesions as Malignant or other masses (e.g. Cysts). Over a database of 145 volumes, with 36 biopsy verified lesions, we achieved Area Under the Curve (AUC) values of 92.6% for lesion detection and 89% for lesion characterization.