Computed tomography (CT) with a contrast-enhanced imaging technique is extensively proposed for the assessment and segmentation of multiple organs, especially organs at risk. It is an important factor involved in the decision making in clinical applications. Automatic segmentation and extraction of abdominal organs, such as thoracic organs at risk, from CT images are challenging tasks due to the low contrast of pixel values surrounding other organs. Various deep learning models based on 2D and 3D convolutional neural networks have been proposed for the segmentation of medical images because of their automatic feature extraction capability based on large labeled datasets. In this paper, we proposed a 3D-atrous spatial pyramid pooling (ASPP) module integrated with a proposed 3D DensNet encoder-decoder network for volumetric segmentation to segment abdominal organs from CT. The proposed network used a 3D-ASPP block to capture spatial information in multiscale input feature maps from the decoder side. We also proposed a 3D-ASPP block with a 3D DensNet network for automatically processing 3D medical volumetric images. The proposed hybrid network was named 3D-ASPPDN for volumetric segmentation via CT medical imaging. We tested our proposed approach on a public dataset, Thoracic Organs at Risk (SegTHOR) 2019. The proposed solution showed excellent performance in comparison with other existing state-of-the-art DL methods. The proposed method achieved Dice scores 97.89 on the SegTHOR dataset. Results presented that 3D-ASPPDN exhibited enhanced performance in volumetric biomedical segmentation. The proposed model could be used for volumetric segmentation in clinical applications to diagnose problems in multi class organs. © This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/