针对非洲脑胶质瘤影像数据少、设备差异大，提出新模型提升分割准确率。
Domain-Adaptive Transformer for Data-Efficient Glioma Segmentation in Sub-Saharan MRI
- 用直方图匹配和放射组学特征分层采样，应对扫描仪差异。
- 在非洲临床数据上分割精度显著提升，边界定位更准。
- 适合医疗资源匮乏地区使用，尤其关注脑瘤精准诊断。
脑胶质瘤分割对诊疗至关重要，但在撒哈拉以南非洲因磁共振设备有限且成像协议异质，导致严重域偏移，难以实现精准分割。本文提出SegFormer3D-plus，一种基于放射组学引导的Transformer架构，旨在应对域变化下的鲁棒分割。方法包括：(1) 采用直方图匹配实现跨扫描仪强度归一化；(2) 基于主成分分析降维的k均值聚类提取放射组学特征，实现域感知分层采样；(3) 双路径编码器结合频率感知特征提取与空间-通道注意力机制；(4) 使用复合Dice-Cross-Entropy损失函数优化边界细节。模型在BraTS 2023预训练，并在BraTS-Africa数据集上微调，显著提升了在异构非洲临床扫描中的肿瘤亚区划分与边界定位能力，验证了放射组学引导域适应在资源受限环境中的价值。
原文摘要 · Abstract (English)
Glioma segmentation is critical for diagnosis and treatment planning, yet remains challenging in Sub-Saharan Africa due to limited MRI infrastructure and heterogeneous acquisition protocols that induce severe domain shift. We propose SegFormer3D-plus, a radiomics-guided transformer architecture designed for robust segmentation under domain variability. Our method combines: (1) histogram matching for intensity harmonization across scanners, (2) radiomic feature extraction with PCA-reduced k-means for domain-aware stratified sampling, (3) a dual-pathway encoder with frequency-aware feature extraction and spatial-channel attention, and (4) composite Dice-Cross-Entropy loss for boundary refinement. Pretrained on BraTS 2023 and fine-tuned on BraTS-Africa data, SegFormer3D-plus demonstrates improved tumor subregion delineation and boundary localization across heterogeneous African clinical scans, highlighting the value of radiomics-guided domain adaptation for resource-limited settings.
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