An adaptive tissue characterization network for model-free visualization of dynamic contrast-enhanced magnetic resonance image data.

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has become an important source of information to aid cancer diagnosis. Nevertheless, due to the multi-temporal nature of the three-dimensional volume data obtained from DCE-MRI, evaluation of the image data is a challenging task and tools are required to support the human expert. We investigate an approach for automatic localization and characterization of suspicious lesions in DCE-MRI data.

Evaluation of radiological features for breast tumour classification in clinical screening with machine learning methods.

Objective: In this work, methods utilizing supervised and unsupervised machine learning are applied to analyze radiologically derived morphological and calculated kinetic tumour features. The features are extracted from dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) time-course data.

Material: The DCE-MRI data of the female breast are obtained within the UK Multicenter Breast Screening Study.