With rapid development of pulmonary MRI techniques, increasingly useful morphological and functional information can be obtained, such as pulmonary perfusion, ventilation and gas uptake through hyperpolarized-gas MRI. Identification of lung anatomy is usually the first step for quantitative analysis. In the work, we proposed and validated a new approach for automatic segmentation of lung anatomy from proton MRI based on 3D U-Net structure. The new method had a relatively consistent performance in all subjects (dice overlap 0.90-0.97). Its future application for anatomical based analysis of structural and/or functional pulmonary MRI data needs further validation in larger number of data.