基于不确定性引导的脑肿瘤分割网络，提升边界模糊区域的分割精度。
UPMAD-Net: A Brain Tumor Segmentation Network with Uncertainty Guidance and Adaptive Multimodal Feature Fusion
- 融合多尺度特征与自适应注意力机制，捕获全局上下文信息。
- 在BraTS2021上达到ET 89.18%、WT 93.67%、TC 91.23%的分割精度。
- 通过蒙特卡洛丢弃估计不确定度，增强低置信区域的鲁棒性。
脑肿瘤分割对诊断与治疗具有重要意义，但其不规则形状、模糊边界和高变异性仍带来挑战。本文提出一种结合深度学习与区域生长先验知识的脑肿瘤分割方法。利用多尺度特征融合（MSFF）模块与自适应注意力机制（AAM）提取多尺度特征并捕捉全局上下文信息。为提升模型在低置信区域的鲁棒性，采用蒙特卡洛丢弃（MC Dropout）进行不确定性估计。大量实验表明，该方法在BraTS数据集上表现优异：在BraTS2021测试集上，增强肿瘤（ET）、全肿瘤（WT）、肿瘤核心（TC）的分割Dice分数分别为89.18%、93.67%、91.23%；在BraTS2019验证集上，对应分数为87.43%、90.92%、90.40%。消融实验证明各模块均对性能提升有关键贡献。
原文摘要 · Abstract (English)
Background: Brain tumor segmentation has a significant impact on the diagnosis and treatment of brain tumors. Accurate brain tumor segmentation remains challenging due to their irregular shapes, vague boundaries, and high variability. Objective: We propose a brain tumor segmentation method that combines deep learning with prior knowledge derived from a region-growing algorithm. Methods: The proposed method utilizes a multi-scale feature fusion (MSFF) module and adaptive attention mechanisms (AAM) to extract multi-scale features and capture global contextual information. To enhance the model's robustness in low-confidence regions, the Monte Carlo Dropout (MC Dropout) strategy is employed for uncertainty estimation. Results: Extensive experiments demonstrate that the proposed method achieves superior performance on Brain Tumor Segmentation (BraTS) datasets, significantly outperforming various state-of-the-art methods. On the BraTS2021 dataset, the test Dice scores are 89.18% for Enhancing Tumor (ET) segmentation, 93.67% for Whole Tumor (WT) segmentation, and 91.23% for Tumor Core (TC) segmentation. On the BraTS2019 validation set, the validation Dice scores are 87.43%, 90.92%, and 90.40% for ET, WT, and TC segmentation, respectively. Ablation studies further confirmed the contribution of each module to segmentation accuracy, indicating that each component played a vital role in overall performance improvement. Conclusion: This study proposed a novel 3D brain tumor segmentation network based on the U-Net architecture. By incorporating the prior knowledge and employing the uncertainty estimation method, the robustness and performance were improved. The code for the proposed method is available at https://github.com/chenzhao2023/UPMAD_Net_BrainSeg.
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