Early treatment of pancreatic cancer is critical for improving patient survival rates. Accurate segmentation of the pancreas and its tumors in CT images is essential for developing effective clinical strategies. However, this task remains highly challenging due to the small size, low contrast and significant anatomical variability of pancreatic tumors. To overcome these challenges, DMSD-Net is introduced as an innovative segmentation model built upon nnU-Net. It consists of two key components: the DMSDA Block and the DMSD Attention Mechanism. The DMSDA Block adjusts local features with global context, while the DMSD Attention Mechanism adaptively modifies the receptive field, improving the model’s capability to detect intricate and irregular targets. This architecture effectively combines global and local information, enhancing the model’s ability to capture fine details in medical images. We evaluated DMSD-Net on two public datasets, MSD and NIH. The results demonstrate that our model surpasses current leading segmentation methods. Furthermore, ablation studies confirm the individual contributions of the DMSDA Block and DMSD Attention Mechanism. In conclusion, our study shows that DMSD-Net supports the diagnosis and preoperative planning of pancreatic cancer, laying a foundation for future research and clinical use.