A Novel Method For Segmentation Of Breast Masses Based On Mammography Images

The accurate segmentation of breast masses in mammography images is a key step in the diagnosis of early breast cancer. To solve the problem of various shapes and sizes of breast masses, this paper proposes a cascaded UNet architecture, which is referred to as CasUNet. CasUNet contains six UNet subnetworks, the network depth increases from 1 to 6, and the output features between adjacent subnetworks are cascaded. Furthermore, we have integrated the channel attention mechanism based on CasUNet, hoping that it can focus on the important feature maps. Aiming at the problem that the edges of irregular breast masses are difficult to segment, a multi-stage cascaded training method is presented, which can gradually expand the context information of breast masses to assist the training of the segmentation model. To alleviate the problem of fewer training samples, a data augmentation method for background migration is proposed. This method transfers the background of the unlabeled samples to the labeled samples through the histogram specification technique, thereby improving the diversity of the training data. The above method has been experimentally verified on two datasets, INbreast and DDSM. Experimental results show that the proposed method can obtain competitive segmentation performance.