Automated Multimodal Computer Aided Detection Based on a 3D-2D Image Registration

Computer aided detection (CADe) of breast cancer is mainly focused on monomodal applications. We propose an automated multimodal CADe approach, which uses patient-specific image registration of MRI and X-ray mammography to estimate the spatial correspondence of tissue structures. Then, based on the spatial correspondence, features are extracted from both MRI and X-ray mammography. As proof of principle, distinct regions of interest (ROI) were classified into normal and suspect tissue. We investigated the performance of different classifiers, compare our combined approach against a classification with MRI features only and evaluate the influence of the registration error. Using the multimodal information, the sensitivity for detecting suspect ROIs improved by 7% compared to MRI-only detection. The registration error influences the results: using only datasets with a registration error below 10mm, the sensitivity for the multimodal detection increases by 10% to a maximum of 88 %, while the specificity remains constant. We conclude that automatically combining MRI and X-ray can enhance the result of a CADe system.