DB-SNet: A dual branch network for aortic component segmentation and lesion localization

Abstract

Accurate segmentation of aortic components, such as lumen, calcification, and false lumen, and associated lesions, including aneurysm, stenosis, and dissection in CT angiography (CTA) scans is crucial for cardiovascular diagnosis and treatment planning. However, most existing automated methods generate binary masks with limited clinical utility and rely on separate computational pipelines for anatomical and lesion segmentation, resulting in higher resource demands. To address these limitations, we propose DB-SNet, a dual-branch 3D segmentation network based on the MedNeXt architecture. The model incorporates a shared encoder and task-specific decoders, enhanced by a novel channel-space cross-fusion module that facilitates effective feature interaction between the two branches. A systematic ablation study was conducted to assess the impact of different backbone architectures, information interaction strategies, and loss weight configurations on dual-task performance. Evaluated on 435 multi-center CTA cases for training and 493 external cases for validation, DB-SNet outperformed 15 state-of-the-art models, achieving the highest average scores on the Dice Similarity Coefficient (DSC: 0.615) and Intersection over Union (IoU: 0.524) metrics. Compared to the current best-performing method (MedNeXt), DB-SNet reduced model parameters by 64.8 % and computational complexity by 36.4 %, while achieving a 30.801 × inference speedup (37.985 s vs. 1170 s for manual annotation). This work introduces a new paradigm for efficient and integrated aortic analysis. By balancing model efficiency and accuracy, DB-SNet offers a robust solution for real-time, resource-constrained clinical environments. Our dataset and code can be accessed at https://github.com/yml-bit/DB-SNet.