Spatial Enhanced Rotation Aware Network for Breast Mass Segmentation in Digital Mammogram

Abstract

Breast cancer is the most common cancer with highest mortality risk among the female worldwide and breast mass is the most effective sign for cancer identification. Thus, accurate segmentation of breast mass is regarded as a key step to reduce the death rate. Traditional segmentation methods require prior knowledge and manually set parameters, while recent studies prefer to construct neural networks based on feature reuse. However, breast mass can display in different orientations and the spatial context is complex, which makes the segmentation remain a challenging task. For these concerns, we propose a Spatial Enhanced Rotation Aware Network (SERAN) for automatic breast mass segmentation. SERAN consists of two critical components: 1) a residual attention encoder with spatial enhancement mechanism for effective feature extraction, and 2) a decoder constructed by multi-stream rotation aware blocks for feature fusion and prediction refinement. To optimize SERAN better and avoid misclassification in background area, a regulation item named Inside-outside Loss (IOL) is used in training procedure. The experimental results tested on a representative subset of Digital Database for Screening Mammography (DDSM) dataset show that SERAN outperforms state-of-the-art methods among most adopted evaluation metrics.