Ho Candy P S, Tromans Christopher, Schnabel Julia A, Brady Michael
Wolfson Medical Vision Lab, Department of Engineering Science, University of Oxford, Parks Road, OX1 3PJ, UK.
Annu Int Conf IEEE Eng Med Biol Soc. 2010;2010:3166-9. doi: 10.1109/IEMBS.2010.5627398.
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.
微钙化的检测、微钙化簇的重建以及随后将其分类为恶性和良性是乳腺癌早期检测中的重要任务。数字乳腺断层合成（DBT）在此类任务中提供了新的机遇。通过利用DBT中的多个投影并运用DBT的几何原理，我们开发了一种基于极线曲线的方法来处理这些任务。该方法提高了检测的灵敏度和特异性；为微钙化三维位置的估计提供了信息；并有助于分类。我们生成了15个模拟数据集，每个数据集都有一个基于椭球体形状的微钙化簇。我们估计每个簇中微钙化的三维位置，并将这些簇重建为椭球体。我们根据椭球体的参数将每个簇分类为恶性或良性。将分类结果与真实情况进行比较。我们的结果表明，微钙化实际三维位置与估计三维位置之间以及椭球体实际参数与估计参数之间的偏差足够小，以至于分类结果100%正确。这证明了三维簇分类的可行性。
