Breast Segmentation in MRI via U-Net Deep Convolutional Neural Networks

Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI) has demonstrated, in recent years, a great potential as a complementary diagnostic method for early detection and diagnosis of breast cancer. However, due to the large amount of data, DCE-MRI manual inspection is error prone and can hardly be handled without the use of a Computer Aided Diagnosis (CAD) system. In a typical CAD processing, the segmentation of the breast parenchyma is a crucial stage aimed to reduce computational effort and to increase reliability. In the last years, deep convolutional networks have outperformed the state-of-the-art in many visual tasks, such as image classification and object recognition. However, very few proposals based on a deep learning approach have been applied so far for segmentation tasks in the biomedical field. The aim of this work is to apply a suitably modified convolutional neural network for fully-automating the non-trivial breast tissues segmentation task in 3D MR data, in order to accurately segment breast parenchyma from the air and other tissues (such as chest-wall). The proposed approach has been validated over 42 DCE-MRI studies. The median segmentation accuracy and Dice similarity index were 98.93 (±0, 15) and 95.90 (±0, 74) respectively with p<;0.05, and 100% of neoplastic lesion coverage.