A U-Net Ensemble for breast lesion segmentation in DCE MRI. (January 2022) - Khaled, Roa'a; Vidal, Joel; Vilanova, Joan C; Martí, Robert
Abstract: Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) has been recognized as an effective tool for Breast Cancer (BC) diagnosis. Automatic BC analysis from DCE-MRI depends on features extracted particularly from lesions, hence, lesions need to be accurately segmented as a prior step. Due to the time and experience required to manually segment lesions in 4D DCE-MRI, automating this task is expected to reduce the workload, reduce observer variability and improve diagnostic accuracy. In this paper we propose an automated method for breast lesion segmentation from DCE-MRI based on a U-Net framework. The contributions of this work are the proposal of a modified U-Net architecture and the analysis of the input DCE information. In that sense, we propose the use of an ensemble method combining three U-Net models, each using a different input combination, outperforming all individual methods and other existing approaches. For evaluation, we use a subset of 46 cases from the TCGA-BRCA dataset, a challenging and publicly available dataset not reported to date for this task. Due to the incomplete annotations provided, we complement them with the help of a radiologist in order to include secondary lesions that were not originally segmented. The proposed ensemble method obtains a mean Dice Similarity Coefficient (DSC) of 0.680 (0.802 for main lesions) which outperforms state-of-the art methods using the same dataset, demonstrating the effectiveness of our method considering the complexity of the dataset. Highlights: A promising U-Net ensemble method for segmenting breast lesions from 4D DCE MRI. Deep Learning surpasses traditional ML in segmenting 3D breast lesions from DCE MRI. 3D U-Net with residual blocks outperform other state-of-the-art architectures. DCE MRI effectiveness for fully automatic breast cancer segmentation and diagnosis. Utilizing more than one DCE MRI temporal image provides better lesion segmentation.