The authors of this study recognized the potential of multiparametric positron emission tomography/magnetic resonance imaging (mpPET/MRI) for detecting and classifying breast lesions. To better understand computer-aided segmentation and diagnosis (CAD) features, the authors introduced a data-driven machine learning approach for a CAD system that enables the assessment of the relevance of mpPET/MRI features on segmentation and classification accuracy.
Key points:
- The positron emission tomography/magnetic resonance imaging (PET/MRI) computer-aided segmentation and diagnosis (CAD) system automatically detects, segments, and classifies breast lesions.
- Automatic lesion segmentation was accurate and improved with information from all modalities.
- A small number of features mainly from dynamic contrast-enhanced MRI achieves high classification accuracies.
- The PET/MRI-CAD system allows exploring the value of different imaging modalities and features.
Authors: Wolf-Dieter Vogl, Katja Pinker, Thomas H. Helbich, Hubert Bickel, Günther Grabner, Wolfgang Bogner, Stephan Gruber, Zsuzsanna Bago-Horvath, Peter Dubsky, Georg Langs
