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...
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Dokumenttyp: | Kapitel/Artikel Konferenzschrift |
| Sprache: | Englisch |
| Veröffentlicht: |
2017
|
| 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-Zugang: | Verlag, Volltext: http://dx.doi.org/10.1007/978-3-319-67558-9_42
|
| Verfasserangaben: | T. Hopp, P. Cotic Smole, N.V. Ruiter |
| Zusammenfassung: | 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. |
|---|---|
| Beschreibung: | Gesehen am 05.09.2018 |
| Beschreibung: | Online Resource |
| ISBN: | 9783319675589 |
| DOI: | 10.1007/978-3-319-67558-9_42 |