用集成模型提升资源有限地区脑肿瘤分割准确率
Deep Ensemble approach for Enhancing Brain Tumor Segmentation in Resource-Limited Settings
- 融合U-Net3D、V-Net和MSA-VNet三模型构建深度集成
- 在多数据集训练下，整体分割精度达DICE 0.8521（全肿瘤）
- 适合医疗资源匮乏地区，可降低对专家依赖
脑肿瘤分割是治疗规划的关键步骤，但人工分割耗时且主观性强，尤其在撒哈拉以南非洲地区，医疗系统超负荷且缺乏先进影像设备与放射科专家。利用深度学习自动化分割具有前景。卷积神经网络（CNN），特别是U-Net架构，已展现潜力。然而，跨数据集的泛化能力仍是主要挑战。本研究通过集成UNet3D、V-Net和MSA-VNet模型，针对胶质瘤进行语义分割。先在BraTS-GLI数据集上训练，再用BraTS-SSA数据集微调，显著优于单个模型：肿瘤核心DICE为0.8358，全肿瘤DICE为0.8521，增强部分DICE为0.8167。结果表明，集成方法能有效提升自动分割的准确性与可靠性，尤其适用于资源有限环境。
原文摘要 · Abstract (English)
Segmentation of brain tumors is a critical step in treatment planning, yet manual segmentation is both time-consuming and subjective, relying heavily on the expertise of radiologists. In Sub-Saharan Africa, this challenge is magnified by overburdened medical systems and limited access to advanced imaging modalities and expert radiologists. Automating brain tumor segmentation using deep learning offers a promising solution. Convolutional Neural Networks (CNNs), especially the U-Net architecture, have shown significant potential. However, a major challenge remains: achieving generalizability across different datasets. This study addresses this gap by developing a deep learning ensemble that integrates UNet3D, V-Net, and MSA-VNet models for the semantic segmentation of gliomas. By initially training on the BraTS-GLI dataset and fine-tuning with the BraTS-SSA dataset, we enhance model performance. Our ensemble approach significantly outperforms individual models, achieving DICE scores of 0.8358 for Tumor Core, 0.8521 for Whole Tumor, and 0.8167 for Enhancing Tumor. These results underscore the potential of ensemble methods in improving the accuracy and reliability of automated brain tumor segmentation, particularly in resource-limited settings.
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