Automated multimodal breast CAD based on registration of MRI and two view mammography
Computer aided diagnosis (CAD) of breast cancer is mainly focused on monomodal applications. Here we present a fully automated multimodal CAD, which uses patient-specific image registration of MRI and two-view X-ray mammography. The image registration estimates the spatial correspondence between eac...
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| Main Authors: | , , |
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| Format: | Chapter/Article Conference Paper |
| Language: | English |
| Published: |
2017
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| In: |
Deep learning in medical image analysis and multimodal learning for clinical decision support
Year: 2017, Pages: 365-372 |
| DOI: | 10.1007/978-3-319-67558-9_42 |
| Online Access: | Verlag, Volltext: http://dx.doi.org/10.1007/978-3-319-67558-9_42
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| Author Notes: | T. Hopp, P. Cotic Smole, N.V. Ruiter |
| Summary: | Computer aided diagnosis (CAD) of breast cancer is mainly focused on monomodal applications. Here we present a fully automated multimodal CAD, which uses patient-specific image registration of MRI and two-view X-ray mammography. The image registration estimates the spatial correspondence between each voxel in the MRI and each pixel in cranio-caudal and mediolateral-oblique mammograms. Thereby we can combine features from both modalities. As a proof of concept we classify fixed regions of interest (ROI) into normal and suspect tissue. We investigate the classification performance of the multimodal classification in several setups against a classification with MRI features only. The average sensitivity of detecting suspect ROIs improves by approximately 2% when combining MRI with both mammographic views compared to MRI-only detection, while the specificity stays at a constant level. We conclude that automatically combining MRI and X-ray can enhance the result of a breast CAD system. |
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| Item Description: | Gesehen am 05.09.2018 |
| Physical Description: | Online Resource |
| ISBN: | 9783319675589 |
| DOI: | 10.1007/978-3-319-67558-9_42 |