用三平面注意力增强的U-Net模型，精准分割脑肿瘤并预测生存期。
Attention-gated U-Net model for semantic segmentation of brain tumors and feature extraction for survival prognosis
- 融合残差、循环与三平面结构，提升特征表达能力。
- 全肿瘤分割Dice达0.900，生存预测准确率45.71%。
- 适合需要精准分割与预后分析的临床研究者。
胶质瘤是常见原发性脑肿瘤，其侵袭性、预后和组织学特征差异大，治疗因手术复杂且耗时而困难。本研究提出基于注意力门控的递归残差U-Net（R2U-Net）的三平面（2.5D）模型，用于改进脑肿瘤分割。该模型通过集成残差、循环与三平面架构，在保持计算效率的同时增强特征表示与分割精度，有望辅助更优治疗规划。在BraTS2021验证集上，全肿瘤（WT）分割的骰子相似系数（DSC）达到0.900，性能接近领先模型。此外，三平面网络每平面提取64个特征用于生存天数预测，经人工神经网络（ANN）降维至28维，测试集上实现45.71%准确率、108,318.128均方误差（MSE）和0.338斯皮尔曼等级相关系数（SRC）。
原文摘要 · Abstract (English)
Gliomas, among the most common primary brain tumors, vary widely in aggressiveness, prognosis, and histology, making treatment challenging due to complex and time-intensive surgical interventions. This study presents an Attention-Gated Recurrent Residual U-Net (R2U-Net) based Triplanar (2.5D) model for improved brain tumor segmentation. The proposed model enhances feature representation and segmentation accuracy by integrating residual, recurrent, and triplanar architectures while maintaining computational efficiency, potentially aiding in better treatment planning. The proposed method achieves a Dice Similarity Score (DSC) of 0.900 for Whole Tumor (WT) segmentation on the BraTS2021 validation set, demonstrating performance comparable to leading models. Additionally, the triplanar network extracts 64 features per planar model for survival days prediction, which are reduced to 28 using an Artificial Neural Network (ANN). This approach achieves an accuracy of 45.71%, a Mean Squared Error (MSE) of 108,318.128, and a Spearman Rank Correlation Coefficient (SRC) of 0.338 on the test dataset.
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