Journal of medical imaging (Bellingham, Wash.) · 2026
DDARes-U2Net: a dual-decoder adversarial residual U2Net algorithm for segmentation of COVID-19 pneumonia lesions.
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Li Xiao, Ju Fujiao, Xu Yifei, Chen Haoran
Аннотация
PURPOSE: We aim to overcome the remaining bottlenecks in COVID-19 lesion segmentation from chest CT-namely, blurred lesion boundaries, false-positive responses from vessels or trachea, and the extreme variability of lesion shape, size, and location-by developing a dual-decoder adversarial residual U 2 Net algorithm.
APPROACH: DDARes- U 2 Net , an encoder-dual-decoder architecture, simultaneously performs both lung parenchyma segmentation and lesion segmentation within a unified network. The dual-decoder design incorporates a lung-parenchyma decoder that guides the model to focus specifically on the lung region, eliminating the information loss caused by traditional presegmentation approaches and markedly reducing computational overhead. Furthermore, an edge-loss function is introduced during training to enhance the network's ability to capture fine-grained lesion boundaries. Comparative experiments are conducted under two settings: within-dataset evaluation and cross-dataset evaluation. Ablation studies are performed to validate the contribution of each architectural component.
RESULTS: The proposed DDARes- U 2 Net model, with a relatively modest parameter count of 30.74 M, achieves superior generalization performance and segmentation accuracy compared with state-of-the-art methods. Across both within-center and cross-center experiments on five CT datasets (I to V), DDARes- U 2 Net consistently ranks first for Dice and IoU. The largest gain is observed when the model trained on Dataset IV is tested on Dataset II, delivering + 3.41 pp Dice versus ARes- U 2 Net + + and + 5.51 pp versus ARes- U 2 Net , confirming its superior generalizability and clinical applicability.
CONCLUSIONS: The unified dual-decoder framework with explicit edge-aware supervision provides accurate, robust, and generalizable COVID-19 lesion segmentation, achieving the highest Dice and IoU in both intradataset and cross-center tests on five independent CT cohorts.
APPROACH: DDARes- U 2 Net , an encoder-dual-decoder architecture, simultaneously performs both lung parenchyma segmentation and lesion segmentation within a unified network. The dual-decoder design incorporates a lung-parenchyma decoder that guides the model to focus specifically on the lung region, eliminating the information loss caused by traditional presegmentation approaches and markedly reducing computational overhead. Furthermore, an edge-loss function is introduced during training to enhance the network's ability to capture fine-grained lesion boundaries. Comparative experiments are conducted under two settings: within-dataset evaluation and cross-dataset evaluation. Ablation studies are performed to validate the contribution of each architectural component.
RESULTS: The proposed DDARes- U 2 Net model, with a relatively modest parameter count of 30.74 M, achieves superior generalization performance and segmentation accuracy compared with state-of-the-art methods. Across both within-center and cross-center experiments on five CT datasets (I to V), DDARes- U 2 Net consistently ranks first for Dice and IoU. The largest gain is observed when the model trained on Dataset IV is tested on Dataset II, delivering + 3.41 pp Dice versus ARes- U 2 Net + + and + 5.51 pp versus ARes- U 2 Net , confirming its superior generalizability and clinical applicability.
CONCLUSIONS: The unified dual-decoder framework with explicit edge-aware supervision provides accurate, robust, and generalizable COVID-19 lesion segmentation, achieving the highest Dice and IoU in both intradataset and cross-center tests on five independent CT cohorts.