Warp-based edge feature reinforcement network for medical image segmentation

Background Rapid and accurate segmentation of medical images can provide important guidance in the early stages of life-threatening diseases. Purpose However, fuzzy edges and high similarity with the background in images usually cause undersegmentation or oversegmentation. To solve these problems. Methods We propose a novel edge features-reinforcement (EFR) module that uses relative frequency changes before and after warping images to extract edge information. Then, the EFR module leverages deep features to guide shallow features to produce a band-shaped edge attention map for reinforcing the edge region of all channels. We also propose a multiscale context exploration (MCE) module to fuse multiscale features and to extract channel and spatial correlations, which allows a model to focus on the parts that contribute most to the final segmentation. We construct EFR-Net by embedding EFR and MCE modules on the encoder-decoder architecture. Results We verify EFR-Net's performance with four medical datasets: retinal vessel segmentation dataset DRIVE, endoscopic polyp segmentation dataset CVC-ClinicDB, dermoscopic image dataset ISIC2018, and aortic true lumen dataset Aorta-computed tomography (CT). The proposed model achieves Dice similarity coefficients (DSCs) of 81.61%, 92.87%, 89.87%, and 96.98% on DRIVE, CVC-ClinicDB, ISIC2018, and Aorta-CT, respectively, which are better than those of current mainstream methods. In particular, the DSC of polyp segmentation increased by 3.87%. Conclusion Through quantitative and qualitative research, our method is determined to surpass current mainstream segmentation methods, and EFR modules can effectively improve the edge prediction effect of color images and CT images. The proposed modules are easily embedded in other encoder-decoder architectures, which has the potential to be applied and expanded.