Deep Learning Based Segmentation of Breast Lesions in DCE-MRI

Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) is a popular tool for the diagnosis of breast lesions due to its effectiveness, especially in a high risk population. Accurate lesion segmentation is an important step for subsequent analysis, especially for computer aided diagnosis systems. However, manual breast lesion segmentation of (4D) MRI is time consuming, requires experience, and it is prone to interobserver and intraobserver variability. This work proposes a deep learning (DL) framework for segmenting breast lesions in DCE-MRI using a 3D patch based U-Net architecture. We perform different experiments to analyse the effects of class imbalance, different patch sizes, optimizers and loss functions in a cross-validation fashion using 46 images from a subset of a challenging and publicly available dataset not reported to date, that is the TCGA-BRCA. We also compare the proposed U-Net framework with another state-of-the-art approach used for breast lesion segmentation in DCE-MRI, and report better segmentation accuracy with the proposed framework. The results presented in this work have the potential to become a publicly available benchmark for this task.