MIS-DFH: Dual-Branch Collaborative Medical Image Segmentation with Full-Link Fusion and Hierarchical Supervision

The core challenge of high-precision medical image segmentation lies in modeling the multi-scale fractal self-similarity of human abdominal organs and cardiac structures, especially the blurred boundaries and weak features of low-contrast tissues. Existing CNNs and Transformers fail to simultaneously capture both global fractal topology and high-frequency fractal details, thereby limiting segmentation performance. To address this, we propose MIS-DFH, a dual-branch CNN–Transformer hybrid model that integrates Hybrid Feature Branches, Multi-Fusion Dense Frequency Skip Connections, and hierarchical Deep Supervision, achieving superior multi-scale feature extraction and segmentation performance. Experiments on the Synapse abdominal CT and ACDC cardiac MRI datasets show that MIS-DFH outperforms all compared state-of-the-art methods. Notably, it achieves 79.90% mean DSC and 20.06 mm HD95 on Synapse, representing a 5.2% DSC improvement and 34.3% HD95 reduction over MSLAU-Net, with consistent gains on ACDC. These results validate the model’s superior segmentation accuracy and clinical application value.