SBDF-Net: A versatile dual-branch fusion network for medical image segmentation

• SBDF-Net extract detail information and global context information. • The dual-branch encoder calibrates lesion area at a more fine-grained level. • SGFM obtain rich multi-scale features by integrating cross-scale information. • TDB and TUB modules reduce feature loss. • Extensive evaluation of medical image segmentation on four datasets. In the field of medical image analysis, image segmentation can help doctors diagnose diseases and plan treatments. U-net has become an important network in biomedical image segmentation. Inspired by U-Net, we propose a dual-branch encoder for aggregating multi-scale context information. A novelty Shuffle Grouped Fusion Module is used to fuse cross-scale information between dual branches. In addition, Skip Connection + calibrates the features extracted by encoder to optimize the feature mapping. Finally, Three-branch Down-sampling Block and Two-branch Up-sampling Block are designed to reduce the feature loss produced by sampling operations. We have evaluated the performance of our network on four datasets. The IoU, Dice and Sensitivity of the model reach 86.45%, 92.95% and 93.36% on the 2018 Data Science Bowl dataset, 81.85%, 89.35% and 88.90% on the GLAS dataset, 80.92%, 87.63% and 87.19% on the Kvasir-SEG dataset, 91.54%, 95.48% and 94.72% on the Aortic Dissection dataset. The experimental results show that our proposed model is superior to U-Net and other advanced segmentation networks in many metrics. The proposed model is available at https://github.com/1998supper/SBDF-Net .