The Head and Neck Tumor Segmentation Based on 3D U-Net.

Head and neck cancer is one of the common malignancies. Radiation therapy is primary treatment of this type of cancer. Mapping the target area of the head and neck tumor is the key step to make the appropriate radiotherapy schedule. However, it is a very time consuming and boring work. Therefore, automatic segmenting the head and neck tumor is of the very significant work. This paper adopts the U-Net network used in medical image segmentation commonly to carry out the automatic segmentation to head and neck tumors based on the dual-modality PET-CT images. The 5-fold cross validation experiments are carried out. The average experimental results are 0.764, 7.467, 0.839, and 0.797 in terms of Dice score, HD95, recall, and precision, respectively. The mean of Dice and the median of HD95 on the test set are 0.778 and 3.088, respectively.