用于多中心T1加权卒中分割的原生空间3D CarveMix方法
Native-Space 3D CarveMix for Multi-Site T1w Stroke Segmentation
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- College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）
- University of Alberta（阿尔伯塔大学）
机构由 AI 辅助整理，请以论文原文为准。
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中文总结 AI 辅助
针对多中心未标准化T1w MRI的卒中分割难题，提出结合MedNeXt-L骨干与动态3D CarveMix增强的方法，在ISLES 2026数据集上实现0.648的5折交叉验证Dice分数，较原骨干提升0.018。
中文摘要 AI 辅助
在未进行强度标准化的情况下，对不同扫描仪和扫描方案采集的T1加权（T1w）MRI图像中的缺血性卒中病灶进行分割难度较大，因为病灶本身较为细微，且与脑脊液的强度特征相似。在多中心数据上训练的标准深度学习架构的Dice分数约为0.66，而急性病灶（卒中后≤7天）的表现明显更差，原因是这类样本严重稀缺。我们将MedNeXt-L（k=5）骨干网络与动态3D CarveMix增强技术相结合，该技术在训练过程中会将真实病灶斑块粘贴到健康脑区。通过在每个折次中以受试者级别的拆分隔离动态生成合成病灶位置，模型无需在磁盘上预先生成副本即可看到更多样化的病灶模式。我们在ISLES 2026挑战赛中来自55个临床中心的1453例原生T1w扫描数据上进行评估，我们的方法在500个训练轮次下的5折交叉验证平均Dice分数为0.648，在相同训练预算下较MedNeXt-L骨干网络（0.630）提升了0.018。
英文摘要
Segmenting ischemic stroke lesions on T1-weighted (T1w) MRI acquired across different scanners and protocols without intensity standardization is difficult because lesions are subtle and share intensity characteristics with cerebrospinal fluid. Standard deep learning architectures trained across multiple centers plateau around Dice 0.66, with acute lesions ($\le 7$ days post-stroke) performing substantially worse due to severe sample scarcity. We combine a MedNeXt-L ($k=5$) backbone with on-the-fly 3D CarveMix augmentation that pastes real lesion patches into healthy brain regions during training. By generating synthetic lesion placements dynamically within each fold with subject-level split isolation, the model sees more diverse lesion patterns without requiring pre-generated copies on disk. We evaluate on 1,453 native T1w scans from 55 clinical centers in the ISLES 2026 challenge. Our method achieves a mean 5-fold cross-validation Dice of 0.648 at 500 epochs, a +0.018 improvement over the MedNeXt-L backbone at a matched training budget (0.630)
