<p>Accurate segmentation of the Clinical Target Volume (CTV) and Organs At Risk (OARs) is a prerequisite for effective cervical cancer radiotherapy. However, this task remains challenging due to the low tissue contrast of the CTV and the complex, tubular geometry of gastrointestinal organs. Existing methods often struggle to delineate indistinct boundaries, leading to suboptimal treatment planning. To address this, we propose BA-UNet, a boundary-augmented framework designed to enforce geometric consistency during both feature encoding and optimization. Specifically, the proposed architecture integrates a Boundary-Infused Feature Aggregation (BIFA) module to inject deterministic edge priors into the multi-scale encoder, explicitly preserving high-frequency boundary information that is often lost in deep feature abstraction. Concurrently, we introduce a novel Boundary-Aware Curvature (BAC) loss that utilizes the Hessian matrix to estimate 3D curvature. This loss function penalizes geometric deviations in complex regions, compelling the network to focus on sharp boundary transitions. Validation on our private dataset and the public WORD dataset demonstrates the superiority of our method. BA-UNet achieved a mean Dice Similarity Coefficient (DSC) of 85.41% and a 95% Hausdorff Distance (HD95) of 11.07 mm on the internal dataset, significantly outperforming state-of-the-art methods, particularly in challenging anatomical structures.</p>