Abstract
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 \(10\,mm\), 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.
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Hopp, T., Neupane, B., Ruiter, N.V. (2016). Automated Multimodal Computer Aided Detection Based on a 3D-2D Image Registration. In: Tingberg, A., Lång, K., Timberg, P. (eds) Breast Imaging. IWDM 2016. Lecture Notes in Computer Science(), vol 9699. Springer, Cham. https://doi.org/10.1007/978-3-319-41546-8_50
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DOI: https://doi.org/10.1007/978-3-319-41546-8_50
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