Medical physics · 2026
Anatomy-aware fusion attention network for high-precision prostate MRI segmentation.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Wang Yulong, Huang Hui, Ma Yan, Zhu Hongqing, Wen Chunlin, Yuan Feiniu, Huang Bingcang
Аннотация
BACKGROUND: Accurate prostate segmentation in Magnetic Resonance Imaging (MRI) is crucial for clinical diagnosis and treatment. However, it remains a challenging task due to low soft-tissue contrast and structural ambiguity in the gland's appearance. While recent deep learning methods have focused on refining network structures, they often neglect the inherent anatomical consistency of the prostate.
PURPOSE: To address this limitation, a novel segmentation framework integrating an Anatomy-Aware Fusion Attention (AAFA) module is proposed.
METHODS: By leveraging anatomical templates, our approach establishes a multilevel feature cross-attention mechanism that enhances global contextual modeling of prostate regions. Additionally, we introduce a Phased Learning strategy that progressively trains the model to mitigate the adverse effects of invalid or noisy samples commonly found in clinical MRI data.
RESULTS: Extensive ablation studies on the PROMISE12 dataset validate the contribution of each component to overall performance. Comparison experiments on both PROMISE12 and MSD Prostate datasets show that our method consistently outperforms existing approaches across key metrics, such as Dice similarity coefficient (DSC), Intersection over Union (IoU), Precision and 95% Hausdorff distance (HD95).
CONCLUSIONS: These results confirm the robustness and strong generalization capability of the proposed framework in challenging clinical segmentation tasks.
PURPOSE: To address this limitation, a novel segmentation framework integrating an Anatomy-Aware Fusion Attention (AAFA) module is proposed.
METHODS: By leveraging anatomical templates, our approach establishes a multilevel feature cross-attention mechanism that enhances global contextual modeling of prostate regions. Additionally, we introduce a Phased Learning strategy that progressively trains the model to mitigate the adverse effects of invalid or noisy samples commonly found in clinical MRI data.
RESULTS: Extensive ablation studies on the PROMISE12 dataset validate the contribution of each component to overall performance. Comparison experiments on both PROMISE12 and MSD Prostate datasets show that our method consistently outperforms existing approaches across key metrics, such as Dice similarity coefficient (DSC), Intersection over Union (IoU), Precision and 95% Hausdorff distance (HD95).
CONCLUSIONS: These results confirm the robustness and strong generalization capability of the proposed framework in challenging clinical segmentation tasks.