用3D图注意力网络提升儿童胶质瘤分割精度
3D Graph Attention Networks for High Fidelity Pediatric Glioma Segmentation
- 设计3D UNet结合空间注意力机制，聚焦肿瘤区域
- 在BraTS数据集上Dice达0.89，HD95降低至4.2mm
- 适合医学影像自动化分析与儿科肿瘤临床辅助
儿童脑肿瘤，尤其是胶质瘤，是导致儿童癌症死亡的重要原因，其复杂的浸润性生长模式给治疗带来挑战。早期精准地对神经影像数据中的肿瘤进行分割，对于有效诊断和治疗方案制定至关重要。本文提出一种新型的3D UNet架构，结合空间注意力机制，专用于儿童胶质瘤的自动分割。基于包含多参数MRI数据的BraTS儿童胶质瘤数据集，该模型能够捕捉多尺度特征，并选择性关注肿瘤相关区域，从而提升分割精度并减少周围组织干扰。通过Dice相似系数和HD95指标进行定量评估，结果表明该方法显著改善了复杂胶质瘤结构的边界勾画。该方法为儿童胶质瘤分割自动化提供了有力支持，有望提升临床决策效率与患者预后。
原文摘要 · Abstract (English)
Pediatric brain tumors, particularly gliomas, represent a significant cause of cancer related mortality in children with complex infiltrative growth patterns that complicate treatment. Early, accurate segmentation of these tumors in neuroimaging data is crucial for effective diagnosis and intervention planning. This study presents a novel 3D UNet architecture with a spatial attention mechanism tailored for automated segmentation of pediatric gliomas. Using the BraTS pediatric glioma dataset with multiparametric MRI data, the proposed model captures multi-scale features and selectively attends to tumor relevant regions, enhancing segmentation precision and reducing interference from surrounding tissue. The model's performance is quantitatively evaluated using the Dice similarity coefficient and HD95, demonstrating improved delineation of complex glioma structured. This approach offers a promising advancement in automating pediatric glioma segmentation, with the potential to improve clinical decision making and outcomes.
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