<p>In medical image segmentation tasks, challenges such as noise interference, blurred boundaries of complex lesions, and irregular lesion shapes are often encountered. In recent years, diffusion models have demonstrated significant advantages in various generative tasks due to their powerful data modeling ability and denoising performance. To address these issues in medical image segmentation, this paper proposes a diffusion model-based medical image segmentation network, DGENet. DGENet deeply integrates Graph Convolutional Networks (GCN) and diffusion models, systematically combining structured guidance and the denoising process for the first time, overcoming the limitations of traditional methods in handling blurred boundaries and irregular lesion segmentation. To better address the mismatch between semantic features and noise features, we introduce the Spatial-Semantic Enhancement Module (SSEM) to ensure the accuracy of the diffusion process. Furthermore, to overcome the unreasonable equal treatment of deep and shallow features in the Denoising-U-Net encoder, and to optimize the feature representation and reconstruction process of denoising U-Net, we design the Multi-Scale Feature Adaptive Fusion Module (MFAM) and Context-Aware Semantic Alignment Module (CASAM). MFAM effectively integrates global and local information, while CASAM reduces the impact of noise and irrelevant information, providing more accurate and consistent semantic support for subsequent feature reconstruction. Experimental results on three public datasets show that DGENet excels in medical image segmentation tasks and outperforms other mainstream methods. This paper not only contributes a high-performance medical image segmentation algorithm, but more importantly, its explicit need for large-scale parallel computing and efficient utilization of HPC resources provides a compelling case and research interface for the application of supercomputing in complex AI-driven medical image analysis fields.</p>