Cervical cancer is one of the leading causes of cancer-related death among women globally, and radiotherapy is a common treatment method for cervical cancer. Among the various radiation therapies, brachytherapy, which involves placing the radiation source directly into an area close to the tumor, can deliver a high dose of radiation directly to the tumor, making it more applicable compared to other radiation methods. Accurate segmentation of organs-at-risk is crucial for accurately estimating radiotherapy doses and maximizing protection of normal tissues from radiation damage. However, automatic segmentation of tubular structures, such as the colon and rectum, remains challenging. Factors such as intestinal folds and motion artifacts can affect the segmentation performance, and the presence of the radiation source in brachytherapy can degrade CT image quality, further impacting the segmentation results. This paper proposes a method for the segmentation of tubular organ-at-risk in cervical cancer based on centerline and distance map information. By enhancing the network’s understanding of anatomical structures, the method improves the identification of the topological structure of tubular organs and their spatial relationships within the human body, thus improving segmentation accuracy and optimizing radiotherapy dose distribution. Through experimental evaluation on a cervical cancer brachytherapy dataset, performance analysis was conducted using metrics such as Dice similarity coefficient (DSC), intersection over union (IoU), Recall, and 95% Hausdorff distance (HD95). The experimental results show that the proposed method outperforms the baseline network ResUNet in most metrics, specifically with a DSC of 71.58%, an IoU of 52.12%, a Recall of 79.03%, which improve by 11.29%, 7.84% and 12.70%, respectively, compared to ResUNet. The HD95 is 10.06, which is a decrease of 1.76 compared to ResUNet. The results indicate that the proposed method effectively improves the segmentation accuracy of the colon and rectum in cervical cancer brachytherapy CT images, reducing the impact of complex organs and image quality on the segmentation results.