Accurate segmentation of the aorta and its 23 substructures is crucial for planning endovascular interventions and treating aortic pathologies. However, deep learning approaches often face practical limits when handling high-resolution 3D computed tomography angiography (CTA). We propose a two-stage framework, AortaSeg, which integrates a 2D object detector with a 3D patch-based segmentation network to reduce memory usage without sacrificing accuracy. Stage 1 localizes anatomical regions using a YOLOv7-based detector guided by reference organs from TotalSegmentator, and Stage 2 applies section-specific 3D DynUNets to cropped sub-volumes. This modular design enables efficient processing of large CTA volumes and improves focus on challenging substructures. On the hidden test set of the MICCAI 2024 AortaSeg Challenge, AortaSeg achieved a Dice Similarity Coefficient (DSC) of 0.767 ± 0.032 and a Normalized Surface Distance (NSD) of 0.797 ± 0.037, ranking 5th among 32 teams, demonstrating a favorable balance between performance and efficiency.