Automatic enhancing objects detection and segmentation in breast DCE-MRI

The aim of our work is to detect any enhancing object of interest for reporting purposes. A deep learning approach combined with a multi-constructor and multi-centric database enabled to initiate the development of a versatile tool in line with clinical real life. The detection problem was addressed using a two-stage three-dimensional cascaded U-Net architecture. A total of 610 single-breast images were used for the model development. Results present interesting score in term of Dice similarity index (0.83) which agree well with the recent literature. Discussion section focuses on the potential benefit in the use of a recently reported loss function.