In recent years, CNNs have demonstrated remarkable success in medical image processing due to their powerful feature extraction capabilities. However, their inherent limitation hampers their ability to learn global features. In contrast, Transformers excel at modeling long-range dependencies, offering a solution to this limitation. Yet, the computational demands of self-attention make high-resolution medical image processing-particularly for 3D data—extremely resource-intensive. To address these challenges, we introduce DeTransNet, a hybrid segmentation method that harnesses the strengths of both CNNs and deformable Transformer. First, we introduce a multi-channel feature fusion module (MCFF) that efficiently integrates multi-scale features from CNNs to enhance the input for the Transformer network. Additionally, we propose a deformable attention mechanism to improve feature extraction, enabling the model to capture global information while reducing self-attention complexity. Extensive experiments on the public BCV dataset demonstrate that the proposed method outperforms existing methods.