DRA U-Net: An Attention based U-Net Framework for 2D Medical Image Segmentation

Limited by the size of the dataset, deep learning models for medical image analysis are usually difficult to train well, and the complex deep learning model with large amount of trainable parameters can not achieve good results. At the same time, due to the lack of clear boundaries, especially in the root tips and roots, as well as the huge differences in shape and texture between images from different patients, an overly simple model cannot accurately segment organs. In order to improve the accuracy of organ segmentation for prostate region detection, in this paper we propose an attention based U-Net framework, which includes an attention mechanism and residual feature extraction network. In addition, we also design an improved loss function to improve the training effect for organ segmentation. We conduct several batches of experiments with the prostate dataset PROMISE12 and the pneumothorax dataset SIIM, the experimental results show that significant segmentation accuracy improvement has been achieved by our proposed method compared to other reported approaches.