Cervical cancer is one of the leading causes of cancer-related deaths among women globally, especially in developing countries, where its high incidence and late-stage diagnosis pose significant challenges to treatment. Accurate segmentation of critical organs, such as the colon and rectum, is crucial for the radiotherapy treatment of cervical cancer. Precise segmentation of these organs helps physicians with dose estimation, ensuring the accuracy and effectiveness of radiotherapy plans, and minimizing radiation damage to healthy tissues. However, the automatic segmentation of tubular structures, such as the colon and rectum, remains challenging, especially due to factors like bowel folds and motion artifacts, which lead to poor segmentation results. The complex morphology of the bowel and its low contrast with surrounding tissues make it difficult to identify boundaries, thereby affecting segmentation accuracy. This paper proposes a method for segmenting tubular critical organs in cervical cancer, combining centerline and distance map information to enhance the network"s understanding of anatomical structures and improve the segmentation accuracy of tumors and critical organs. Based on the traditional U-Net architecture, we incorporate centerline and distance map learning into the network, which helps the model better recognize the topological structure of tubular critical organs and their spatial relationships within the body, ultimately improving segmentation precision and optimizing radiotherapy dose distribution. Through experimental evaluation on a cervical cancer dataset, performance analysis was conducted using metrics such as Dice Similarity Coefficient (Dice), Intersection over Union (IoU), Recall, and 95th percentile Hausdorff Distance (HD95). The experimental results show that our method outperforms the traditional U-Net model in all these metrics, with a Dice score of 69.75%, IoU of 54.17%, and Recall of 73.57%, which represent improvements of 30.88%, 29.93%, and 36.91%, respectively, compared to the original 3D-UNet. HD95 is 9.46, which is a reduction of 8.59 compared to the original 3D-UNet. These results demonstrate that accurate segmentation of critical organs not only improves tumor recognition in cervical cancer but also provides important support for radiotherapy dose estimation, optimizes treatment planning, and enhances the safety and effectiveness of treatment.