The automatic segmentation of gross tumor volume (GTV) in PET-CT images using artificial intelligence techniques is one of the challenges that the biomedical field is facing in the last decade. Currently, the images are mainly contoured manually or using approximate strategies such as thresholding, region growing or level set methods in order to outline the lesion and define a surgical plan and consequent therapy in relation to the degree of aggressiveness of the tumor. The use of automatic contouring approaches can facilitate and support nuclear radiologists and oncologists in this task. This thesis project aims to implement an automatic algorithm, based on deep learning methods, that assists physicians in tumor segmentation of the head and neck area, the seventh most common type of cancer in the world, which, in recent years, has seen an increase due mainly to the high use of alcohol and tobacco.