Automatic segmentation of breast masses in DBT images based on RMAU-Net
Accurate breast mass segmentation is important for the diagnosis and treatment of early breast cancer.Digital breast tomosynthesis(DBT)has been widely used for breast cancer screening with a high detection rate for lesions.However,the high breast densities and low contrast in DBT images make the au-tomatic segmentation of breast masses very challenging.In order to efficiently and accurately segment the masses in DBT images,this paper proposes a residual multi-attention U-shaped segmentation network(RMAU-Net),which utilizes a residual structure to avoid performance degradation caused by gradient vanishing.Meanwhile,a deep attention feature fusion module and a multipath high-level feature fusion module are used in the network to improve the feature extraction ability of the network as well as the abili-ty to recognize the boundary of suspicious regions.The RMAU-Net performs segmentationon a private DBT image dataset(DBT_SZ)and achieves a Dice of 86.77％,a sensitivity of 87.84％,and an IOU of 80.15％.In addition,this paper compares RMAU-Net with some advanced segmentation networks.Ex-perimental results show that RMAU-Net can extract mass edges more accurately so that improve the seg-mentation accuracy.
breast mass segmentationdigital breast tomosynthesis imagesdeep learningresidual structureattention mechanism
万方数据
维普
