Nasopharyngeal carcinoma computed tomography (CT) image segmentation is an essential task for diagnosis and treatments of nasopharyngeal carcinoma. However, nasopharyngeal carcinoma cells have various shapes, uneven gray scales, fuzzy boundaries, and complicated shapes of lesion cells, so it is difficult to accurately segment the image. In order to solve this problem, we propose a nasopharyngeal carcinoma CT image segmentation method based on three-dimensional convolutional neural networks (3D CNNs). In our three-dimensional deep convolutional neural network framework, ordinary convolutions with 33 convolution kernel are employed in the first 5 layers, the dilated convolutions with a dilation factor of 2 are employed in the middle 6 layers, and the dilated convolutions with adilation factor of 4 are employed in the last 6 layers. The residual connection is used between every two convolutional layers, and the softmax function is used to classify pixels. Dilated convolutions help to obtain accurate density prediction and fine segmentation maps along object boundaries. Residual connections smooth the information propagation in the deep convolutional neural network and improve the training speed. Experimental results show that the proposed method has better performance than other mainstream methods for nasopharyngeal carcinoma CT image segmentation.