  Head and Neck (H\&N) organ-at-risk (OAR) and tumor segmentations are
essential components of radiation therapy planning. The varying anatomic
locations and dimensions of H\&N nodal Gross Tumor Volumes (GTVn) and H\&N
primary gross tumor volume (GTVp) are difficult to obtain due to lack of
accurate and reliable delineation methods. The downstream effect of incorrect
segmentation can result in unnecessary irradiation of normal organs. Towards a
fully automated radiation therapy planning algorithm, we explore the efficacy
of multi-scale fusion based deep learning architectures for accurately
segmenting H\&N tumors from medical scans.
