针对扫描模糊导致的脑肿瘤分割不准，提出同步去模糊与分割的新模型。
Degradation-Aware Blur-Segmentation of Brain Tumor
- 设计特征域去模糊模块，同步修复运动模糊并平衡图像强度。
- 在BraTS2020上实现93.6%的肿瘤Dice分数，边界精度显著提升。
- 适合临床真实场景中存在图像退化的脑肿瘤精准分割任务。
多模态3D MRI脑肿瘤分割是放疗靶区勾画、手术规划和术后评估的关键步骤。现有方法通常假设图像无伪影，但扫描时患者运动不可避免地引入伪影和模糊，损害边界与纹理特征，导致分割性能下降。为此，我们提出退化感知的模糊-分割网络（DABSeg），一个统一去模糊与精确分割的同步3D多模态MRI分割框架。具体地，提出特征域运动去模糊主干，补偿模糊并重平衡强度；骨干网络嵌入退化感知跨模态交叉注意力与多尺度残差聚合，实现有效模态互补。特别地，采用结合加权Dice与清晰参考重建项的联合损失函数，对小目标施加不平衡权重，增强小病灶及边界区域的学习强度与预测稳定性。在BraTS2020数据集上，于清晰与退化条件下系统对比与消融实验均表明，DABSeg在肿瘤Dice分数与边界精度上超越现有最优方法。结果验证了退化感知跨任务协同学习在提升多模态3D脑肿瘤分割鲁棒性与临床实用性的有效性。源码见：https://github.com/YuchunWang24/DABSeg_ICPR
原文摘要 · Abstract (English)
Multimodal 3D MRI brain tumor segmentation is a pivotal step in radiotherapy target delineation, surgical planning and post-treatment assessment. Existing methods often assume artifact-free MRI images. However, inevitable patient motion during scanning introduces artifacts and blur that degrade boundary and texture features, leading to poor segmentation performance. To bridge this gap, we introduce Degradation-Aware Blur-Segmentation Net (DABSeg), a synchronous deblurring 3D multimodal MRI segmentation network that unifies blur removal and accurate segmentation. Specifically, we propose a feature-domain motion-deblurring stem to compensate for blur and rebalance intensity. Concurrently, the backbone network embeds a blur-aware cross-modal cross-attention module and multi-scale residual aggregation to yield effective modality complementarity. Notably, we optimize a joint loss that combines weighted Dice with a clear-reference reconstruction term, where imbalanced weights are applied to small targets to boost learning intensity and predictive stability for small lesions and border regions. Systematic comparisons and ablation experiments on the BraTS2020 dataset under both clear and degenerative conditions consistently demonstrate that DABSeg surpasses state-of-the-art methods in tumor Dice score and boundary precision. These results validate the effectiveness of degenerative-aware cross-task collaborative learning in improving the robustness and clinical utility of multi-modal 3D brain tumor segmentation under realistic degenerative conditions. The source code is available at https://github.com/YuchunWang24/DABSeg_ICPR
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