During the last few years, deep learning-based techniques have made much progress in the medical image processing field, such as segmentation and registration. The main characteristic of these methods is the large demand of medical images to do model training. However, the acquisition of these data is often difficult, due to the high expense and ethical issues. As a consequence, the lack of data may lead to poor performance and overfitting. To tackle this problem, we propose a data augmentation algorithm in this paper to inpaint the tumor on healthy pediatric brain MRI images to simulate pathological images. Since the growth of tumor may cause deformation and edema of the surrounding tissues which is called the 'mass effect', a probabilistic UNet is applied to mimic this deformation field. Then, instead of directly adding the tumor to the image, the GAN-based method is applied to transfer the mask to the image and make it more plausible, both visually and anatomically. Meanwhile, the annotations of the different brain tissues are also obtained by employing the deformation field to the original labels. Finally, the synthesized image together with the real dataset is trained to do the tumor segmentation task, and the results indicate a statistical improvement in accuracy.