A robust and extendable framework towards fully automated diagnosis of nonmass lesions in breast DCE-MRI

Abstract

Diagnosis of breast nonmass lesions, most notably ductal carcinoma in situ, is challenging. Recent studies show that dynamic contrast enhanced MRI achieves high sensitivity in diagnosis of nonmass lesions. Unlike successfully applied to diagnose mass lesions, particularly kinetic features are reported to be less effective in discriminating nonmass lesions. It is even difficult for human observers to differentiate nonmass lesions against the enhancing parenchymal or benign lesions due to their sometimes similar morphology and contrast kinetics. Towards an automated computer-aided diagnosis system of nonmass lesions, we implemented an extendable and completely automated framework that is efficient and modularized, aiming to discriminate detected suspicious regions into malignant and benign. The entire framework consists of five sequentially executed modules: motion correction, segmentation of breast regions, detection of suspicious regions, feature extraction, and knowledge-based analysis of suspicious regions. A preliminary test was performed on a data set collecting 162 nonmass lesions extracted from 67 patients, which achieved an area under ROC curve value of 0.74 for malignant lesions.