Computer-aided detection of masses in digital tomosynthesis mammography: combination of 3D and 2D detection information

We are developing a computer-aided detection (CAD) system for masses on digital breast tomosynthesis mammograms (DBTs). The CAD system includes two parallel processes. In the first process, mass detection and feature analysis are performed in the reconstructed 3D DBT volume. A mass likelihood score is estimated for each mass candidate using a linear discriminant (LDA) classifier. In the second process, mass detection and feature analysis are applied to the individual projection view (PV) images. A mass likelihood score is estimated for each mass candidate using another LDA classifier. The mass likelihood images derived from the PVs are back-projected to the breast volume to estimate the 3D spatial distribution of the mass likelihood scores. The mass likelihood scores estimated by the two processes at the corresponding 3D location are then merged and evaluated using FROC analysis. In this preliminary study, a data set of 52 DBT cases acquired with a GE prototype system at the Massachusetts General Hospital was used. The LDA classifiers with stepwise feature selection were designed with leave-one-case-out resampling. In an FROC analysis, the CAD system for detection in the DBT volume alone achieved test sensitivities of 80% and 90% at an average FP rate of 1.6 and 3.0 per breast, respectively. In comparison, the average FP rates of the combined system were 1.2 and 2.3 per breast, respectively, at the same sensitivities. The combined system is a promising approach to improving mass detection on DBTs.