Brain tumor segmentation from medical images is a prerequisite to provide a quantitative and intuitive reference for clinical diagnosis and treatment. Manual segmentation depends on clinicians experience, and is laborious and time consuming. To tackle these issues, we proposed an encoder-decoder neural network, i.e. deep supervised 3D Squeeze-and-Excitation V-Net (DSSE-V-Net) to segment brain tumors automatically. We modified V-Net by adding batch normalization and using bottom residual block to make the network deeper. Then we incorporated a squeeze & excitation (SE) module in the modified V-Net by adding the SE block in each stage of the encoder and decoder, respectively. We also integrated 3D deep supervision seamlessly into the network to accelerate convergence. We evaluated our model on the public BraTS 2017 dataset for brain tumor segmentation. Our model outperformed both 3D U-Net and modified V-Net, and obtained highly competitive performance compared with those methods winning in the BraTS 2017 challenge. This project is implemented with MATLAB software.