This work was published as an extended abstract for the IWBI2020 workshop. It was carried out during a three month MSc internship at Screenpoint Medical under supervision of Jaap Kroes and Michiel Kallenberg.
Semantic segmentation of breast images is typically performed as a preprocessing step for breast cancer detection by Computer Aided Diagnosis (CAD) systems. While most literature on region segmentation is based on conventional techniques like line estimation, thresholding and atlas-based approaches, such methods may have problems with generalisation. This paper investigates a robust multi-vendor breast region segmentation system for full field digital mammograms (FFDM) and digital breast tomography (DBT) using a U-Net neural network. Additionally, the effect of adding attention gates to the U-Net architecture was analysed. The proposed networks were trained and tested in a cross-validation setting on in-house FFDM/DBT data and the public INbreast datasets, comprising over 10.000 FFDM and 3.500 DBT images from five different vendors. Dice scores were obtained in the range 0.978 -- 0.985, with slightly higher scores for the architecture that includes attention gates.