Learning Pre- and Post-contrast Representation for Breast Cancer Segmentation in DCE-MRI

Abstract

Breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) plays a considerable role in high-risk breast cancer diagnosis and image-based prognostic prediction. The accurate and robust segmentation of cancerous regions is with clinical demands. However, automatic segmentation remains challenging, due to the large variations of cancers in shape and size, and the class-imbalance issue. To tackle these problems, we offer a two-stage framework, which leverages both pre- and post-contrast images for the segmentation of breast cancer. Specifically, we first employ a breast segmentation network, which generates the breast region of interest (ROI) thus removing confounding information from thorax region in DCE-MRI. Furthermore, based on the generated breast ROI, we offer an attention network to learn both pre- and post-contrast representations for distinguishing cancerous regions from the normal breast tissue. The efficacy of our framework is evaluated on a collected dataset of 261 patients with biopsy-proven breast cancers. Experimental results demonstrate our method attains a Dice coefficient of 91.11% for breast cancer segmentation. The proposed framework provides an effective cancer segmentation solution for breast examination using DCE-MRI. The code is publicly available at https://github.com/2313595986/BreastCancerMRI.