Dense Encoder-Decoder Network based on Two-Level Context Enhanced Residual Attention Mechanism for Segmentation of Breast Tumors in Magnetic Resonance Imaging

摘要

Aiming to effective early detection of breast cancer, automatic tumor segmentation based on breast Magnetic Resonance Imaging (MRI) is concentrated by more and more researchers. This paper proposes a dense encoder-decoder network based on two-level context enhanced residual attention mechanism (TLCRAM-DED). With respect to TLCRAM-DED, we design the encoding structure combining two-level residual attention structure with dense block to extract and refine the features of different layers. Meanwhile, a dense multi-scale atrous convolution is used at the end of the encoder to obtain a larger receptive field and enrich the extracted semantic information. Moreover, residual attention structure (RAS) is also used for the refinement during decoding stage, while a long connection formed with the encoder RAS output is applied to supplement the features and to gradually recover the segmentation details. We validated prosed model in the DCE sequence of challenging breast cancer MRI dataset. The average Dice coefficient is up to 81.04%, which outperforms compared state-of-the-arts.