This study investigates the application of an advanced deep learning technique in the segmentation of medical images, focusing on a specific case study: the segmentation of the bladder in Cone Beam Computed Tomography images. The main objective is to develop an accurate and practical approach to delimiting the region of interest, contributing to advances in medical image processing. The methodology adopted uses a set of computed tomography images made available by the radiotherapy sector of the Hospital Universitário de Brasília (HUB), which served to train, test and evaluate the model. The database consists of 1,932 CT images (256  \(\times \)  256) and 1,932 corresponding masks. The chosen architecture, U-Net, was trained using data augmentation strategies to improve its generalization. The results demonstrated the viability of the proposed approach for bladder segmentation, with a Dice Coefficient of 81%. Furthermore, the development of an application integrated with the pretrained model that could provide a practical and accessible tool for Radiotherapy specialists. The qualitative analysis of the segmentations, reinforced by visual examples, highlights the model’s accuracy in locating and contouring the bladder anatomy in Cone Beam - CT images.