Hypothalamus is a small structure of the brain with an important role in sleep, appetite, body temperature regulation and emotion. Some neurological diseases, such as Schizophrenia, Alzheimer and Amyotrophic Lateral Sclerosis (ALS) may be related to hypothalamic volume variation. However, hypothalamic morphological landmarks are not always clear on magnetic resonance (MR) images and manual segmentation can become variable, leading to inconsistent findings in the literature. In this work, we propose a fully automatic segmentation method, with no human interaction, to segment hypothalamus in MR images using convolutional neural networks (CNNs). The best performance was obtained by a consensus model using the majority voting from three 2D-CNNs trained on axial, coronal and sagittal MRI slices, achieving a DICE coefficient of 0.77. To the best of our knowledge, this is the first work to fully automatically segment the hypothalamus.