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.

翻译：背景：血管周围间隙（PVS）扩大常见于神经退行性疾病，包括脑小血管病、阿尔茨海默病和帕金森病。PVS扩大可能提示清除通路受损，目前亟需可靠的PVS检测方法。目的：针对PVS分割任务优化广泛使用的深度学习模型——无需新配置的U-Net（nnU-Net）。方法：对30名健康参与者（平均年龄±标准差：50±18.9岁；女性13名）使用三台MRI扫描仪（3T西门子Tim Trio、3T飞利浦Achieva和7T西门子Magnetom）分别通过三种不同协议采集T1加权MRI图像。在每位参与者的十个轴向切片中手动分割PVS，采用稀疏标注策略完成标注。通过不同图像处理、预处理及基于伪标签的半监督学习策略，共比较了11个模型。采用5折交叉验证评估模型性能，主要性能指标为Dice相似系数（DSC）。结果：体素间距无关模型（平均DSC±标准差=64.3±3.3%）优于将图像重采样至统一分辨率的模型（DSC=40.5-55%）。经过迭代标签清洗后，模型性能显著提升（DSC=85.7±1.2%）。利用来自18个附加数据集的伪标签（n=12,740）进行半监督学习，改善了原始与预测PVS簇计数的一致性（Lin一致性相关系数=0.89，95%置信区间=0.82-0.94）。我们将模型扩展至中脑（DSC=64.3±6.5%）和海马体（DSC=67.8±5%）的PVS分割。结论：本研究提出的深度学习模型为脑MRI中PVS的自动量化提供了稳健且完整的框架。