Radiation therapy to brain tumor requires a delicate balance between delivering adequate tumorcidal dose to the tumor lesion while minimizing radiation to healthy organs. Segmentation of organs at risk (OARs) is time-consuming and subject to inter-observer variability. Auto-segmentation can 1) increase workflow output by lightening segmentation time, and 2) decrease inter-observer variability. The Radiation Oncology Department at the McGill University Health Centre in collaboration with the Montreal Children Hospital treat pediatric patients with brain tumors. Compared to adult population, radiation therapy to pediatric population is higher stake for several reasons.1) As opposed to adult patients with mature anatomy, radiation can several impair normal growth of pediatric patients; 2) There are more brain structures in pediatric patients to consider; 3) Most children brain structures are more sensitive to radiation than those of adults. Thus, accurate segmentation is of great importance. The project is to investigate deep learning algorithms that can accurately auto-segment OARs in pediatric patients undergoing brain radiation therapy.