解决脑肿瘤分割中模态缺失导致的性能波动问题。
AMGFormer: Adaptive Multi-Granular Transformer for Brain Tumor Segmentation with Missing Modalities
- 三模块协同设计，实现模态无关的稳定融合
- 在15种组合下误差小于0.5%，关键指标超89%
- 适合临床部署，单次推理仅需1.2秒
多模态MRI对脑肿瘤分割至关重要，但临床中常存在模态缺失，导致现有方法在不同模态组合下的性能差异超过40%，临床可靠性差。本文提出AMGFormer，通过三个协同模块显著提升稳定性：(1) 四路整合桥（QIB）实现空间自适应融合，确保不同模态组合下预测一致；(2) 多粒度注意力调度器（MGAO）聚焦病灶区域，降低对背景的敏感性；(3) 模态质量感知增强（MQAE）防止劣质序列引发错误传播。在BraTS 2018上，该方法在15种模态组合下实现WT Dice 89.33%、TC 82.70%、ET 67.23%，性能波动低于0.5%，彻底解决稳定性危机。单模态ET分割相对最优方法提升40%-81%。模型在BraTS 2020/2021上表现更优，最高达WT 92.44%、TC 89.91%、ET 84.57%。推理时间仅1.2秒，具备临床应用潜力。代码已开源。
原文摘要 · Abstract (English)
Multimodal MRI is essential for brain tumor segmentation, yet missing modalities in clinical practice cause existing methods to exhibit >40% performance variance across modality combinations, rendering them clinically unreliable. We propose AMGFormer, achieving significantly improved stability through three synergistic modules: (1) QuadIntegrator Bridge (QIB) enabling spatially adaptive fusion maintaining consistent predictions regardless of available modalities, (2) Multi-Granular Attention Orchestrator (MGAO) focusing on pathological regions to reduce background sensitivity, and (3) Modality Quality-Aware Enhancement (MQAE) preventing error propagation from corrupted sequences. On BraTS 2018, our method achieves 89.33% WT, 82.70% TC, 67.23% ET Dice scores with <0.5% variance across 15 modality combinations, solving the stability crisis. Single-modality ET segmentation shows 40-81% relative improvements over state-of-the-art methods. The method generalizes to BraTS 2020/2021, achieving up to 92.44% WT, 89.91% TC, 84.57% ET. The model demonstrates potential for clinical deployment with 1.2s inference. Code: https://github.com/guochengxiangives/AMGFormer.
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