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

ABSTRACT:

Background

Multiparametric 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.

Methods

The CAD incorporated a random forest (RF) classifier combined with mpPET/MRI intensity-based features for lesion segmentation and shape features, kinetic and spatio-temporal texture features, for lesion classification. The CAD pipeline detected and segmented suspicious regio

SUBMITTER: Vogl WD 

PROVIDER: S-EPMC6486931 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature