Accurate pancreatic segmentation for CT scans is increasingly important in the early detection and diagnosis of pancreatic cancer. Though deep neural networks based pancreatic segmentation methods have achieved significant success in recent years, due to the low-contrast and indistinct boundaries of pancreas in CT scans, precise segmentation of small pancreatic tumors remains an extremely challenging task. To address this challenge, we propose a novel network named DeformSegNet (DSN), which is composed of three components: Localization Module, Deformation Field Module and Adaptive Segmentation Module. First of all, Localization Module is employed to capture local Region of Interest(ROI) of small pancreatic lesion. And then local ROI is carried out a Deformation Field Module, which relocate local ROI to the center of a new image and then magnify it to the same size as the original input, enhancing the neighborhood information and texture. After that, Adaptive Segmentation Module is used to implement the fine segmentation to the local image. Finally, the Deformation Field Module is employed again to adjust the output of segmentation to its original size. Experiments are conducted on two datasets: NIH and a private dataset provided by collaborating hospitals. The proposed network achieved Dice Similarity Coefficient (DSC) scores of 87.02±4.12%and 85.94%, respectively, demonstrating that the performance of our proposed method has outperformed most of SOTA models.