This study aimed to develop an automated delineation method of nasopharynx gross tumor volume (GTVnx) for nasopharyngeal carcinoma (NPC) in computed tomography (CT) image for radiotherapy applications. Inspired by ResNet and SENet's strong ability to extract image features, we proposed a modified version of the 3D U-Net model with Res-blocks and SE-block for delineation of GTVnx. Besides, an automatic pre-processing method was proposed to crop the 3D region of interest (ROI) of GTVnx. Radiotherapy simulation CT images and corresponding manually delineated target of 205 NPC patients diagnosed with stage T1-T4 were used as datasets for training. Automated delineation models were generated based on CT combining contrast-enhanced CT (CE-CT) and CT alone, respectively. We compared the automatic delineation results against the manual delineated contours by radiation oncologists with 5-fold cross-validation to evaluate the performance of the proposed model. We also compared with the framework using 3D CNN and 2D DDNN, respectively. Besides, the model generated by one medical group was assessed against the other two separate medical groups. Precision (PR), Sensitivity (SE), Dice Similarity Coefficient (DSC), Average Symmetric Surface Distance (ASSD), and 95% Hausdorff Distance (HD95) are calculated for quantitative evaluation. Experimental results show that the proposed method outperforms other automatic methods on the CT images. Automated delineation models based on CT combining CE-CT is superior to that based on CT alone. The presented method could be useful and robust for the 3D delineation of GTVnx for NPC in CT images during the planning of radiotherapy.