Classification of Clusters of Microcalcifications in Digital Breast Tomosynthesis

The detection of microcalcifications, reconstruction of clusters of microcalcifications and their subsequent classification into malignant and benign are important tasks in the early detection of breast cancer. Digital breast tomosynthesis (DBT) provides new opportunities in such tasks. By utilizing the multiple projections in DBT and using the geometry of DBT, we have developed an approach to them based on epipolar curves. It improves the sensitivity and specificity in detection; provides information for estimation of 3D positions of microcalcifications; and facilitates classification. We have generated 15 simulated datasets, each with a microcalcification cluster based on an ellipsoidal shape. We estimate the 3D positions of the microcalcifications in each of the clusters and reconstruct the clusters as ellipsoids. We classify each cluster as malignant or benign based on the parameters of the ellipsoids. The classification result is compared with the ground truth. Our results show that the deviations between the actual and estimated 3D positions of the microcalcification, and the actual and estimated parameters of the ellipsoids are sufficiently small that the classification results are 100% correct. This demonstrates the feasibility in cluster classification in 3D.