CDA-Net for Automatic Prostate Segmentation in MR Images

Abstract

Automatic and accurate prostate segmentation is an essential prerequisite for assisting diagnosis and treatment, such as guiding biopsy procedures and radiation therapy. Therefore, this paper proposes a cascaded dual attention network (CDA-Net) for automatic prostate segmentation in MRI scans. The network includes two stages of RAS-FasterRCNN and RAU-Net. Firstly, RAS-FasterRCNN uses improved FasterRCNN and sequence correlation processing to extract regions of interest (ROI) of organs. This ROI extraction serves as a hard attention mechanism to focus the segmentation of the subsequent network on a certain area. Secondly, the addition of residual convolution block and self-attention mechanism in RAU-Net enables the network to gradually focus on the area where the organ exists while making full use of multiscale features. The algorithm was evaluated on the PROMISE12 and ASPS13 datasets and presents the dice similarity coefficient of 92.88% and 92.65%, respectively, surpassing the state-of-the-art algorithms. In a variety of complex slice images, especially for the base and apex of slice sequences, the algorithm also achieved credible segmentation performance.