用nnU-Net实现脑MRI中血管周围间隙的自动分割，精度达85.7%。
A Comprehensive Framework for Automated Segmentation of Perivascular Spaces in Brain MRI with the nnU-Net
- 改进nnU-Net模型，采用无体素间距依赖策略提升分割精度。
- 经迭代标注清洗后Dice系数达85.7%，显著优于原始模型。
- 支持中脑与海马区分割，适用于神经退行性疾病研究。
血管周围间隙（PVS）扩大常见于脑小血管病、阿尔茨海默病和帕金森病等神经退行性疾病，可能提示清除通路受损，亟需可靠的检测方法。本研究优化了广泛使用的深度学习模型nnU-Net用于PVS自动分割。在30名健康受试者（平均年龄50±18.9岁，女性13人）中，使用三台MRI扫描仪（3T Siemens Tim Trio、3T Philips Achieva、7T Siemens Magnetom）采集T1加权图像，每名受试者在十层轴向切片上进行手动分割，采用稀疏标注策略。共对比11种模型，涵盖不同图像处理、预处理及半监督学习（伪标签，n=12,740）策略。性能通过5折交叉验证评估，主要指标为骰子相似系数（DSC）。结果表明，体素间距无关模型（均值±标准差DSC=64.3±3.3%）优于重采样至统一分辨率的模型（DSC=40.5–55%）。经迭代标注清洗后，性能大幅提升至85.7±1.2%。半监督学习使原始与预测的PVS簇计数一致性显著提高（林氏一致性相关系数=0.89，95%置信区间0.82–0.94）。模型进一步扩展至中脑（DSC=64.3±6.5%）和海马（DSC=67.8±5%）区域。结论：本研究构建了鲁棒、全面的自动化脑MRI中PVS量化框架。
原文摘要 · Abstract (English)
Background: Enlargement of perivascular spaces (PVS) is common in neurodegenerative disorders including cerebral small vessel disease, Alzheimer's disease, and Parkinson's disease. PVS enlargement may indicate impaired clearance pathways and there is a need for reliable PVS detection methods which are currently lacking. Aim: To optimise a widely used deep learning model, the no-new-UNet (nnU-Net), for PVS segmentation. Methods: In 30 healthy participants (mean$\pm$SD age: 50$\pm$18.9 years; 13 females), T1-weighted MRI images were acquired using three different protocols on three MRI scanners (3T Siemens Tim Trio, 3T Philips Achieva, and 7T Siemens Magnetom). PVS were manually segmented across ten axial slices in each participant. Segmentations were completed using a sparse annotation strategy. In total, 11 models were compared using various strategies for image handling, preprocessing and semi-supervised learning with pseudo-labels. Model performance was evaluated using 5-fold cross validation (5FCV). The main performance metric was the Dice Similarity Coefficient (DSC). Results: The voxel-spacing agnostic model (mean$\pm$SD DSC=64.3$\pm$3.3%) outperformed models which resampled images to a common resolution (DSC=40.5-55%). Model performance improved substantially following iterative label cleaning (DSC=85.7$\pm$1.2%). Semi-supervised learning with pseudo-labels (n=12,740) from 18 additional datasets improved the agreement between raw and predicted PVS cluster counts (Lin's concordance correlation coefficient=0.89, 95%CI=0.82-0.94). We extended the model to enable PVS segmentation in the midbrain (DSC=64.3$\pm$6.5%) and hippocampus (DSC=67.8$\pm$5%). Conclusions: Our deep learning models provide a robust and holistic framework for the automated quantification of PVS in brain MRI.
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