Automatic segmentation and classification of breast lesions through identification of informative multiparametric PET/MRI features

摘要

BackgroundMultiparametric positron emission tomography/magnetic resonance imaging (mpPET/MRI) shows clinical potential for detection and classification of breast lesions. Yet, the contribution of features for computer-aided segmentation and diagnosis (CAD) need to be better understood. We proposed a data-driven machine learning approach for a CAD system combining dynamic contrast-enhanced (DCE)-MRI, diffusion-weighted imaging (DWI), and ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG)-PET.