背景:在磁共振成像(MRI)上可见的血管周围间隙(PVS)是与各种神经系统疾病相关的重要标志物。尽管PVS的定量分析可以提高敏感性并提高研究的一致性,该领域缺乏一种普遍验证的方法来分析来自多中心研究的图像。
方法:我们在使用三大供应商(西门子,GeneralElectric,和飞利浦)。神经网络,MCPVS-Net(多中心PVS分割网络),使用来自40名受试者的数据进行训练,然后在15名受试者的单独队列中进行测试。我们根据为每个扫描仪供应商量身定制的地面实况掩模评估了分割准确性。此外,我们评估了每个扫描仪的分段PVS体积和视觉评分之间的一致性.我们还探讨了PVS体积与各种临床因素之间的相关性,例如年龄,高血压,和白质高强度(WMH)在1020名受试者的较大样本中。此外,mcPVS-Net被应用于包含来自联合成像扫描仪的T1w和T2加权(T2w)图像的新数据集以调查PVS体积是否可以区分具有不同视觉评分的受试者。我们还将mcPVS-Net与先前发布的从T1图像分割PVS的方法进行了比较。
结果:在测试数据集中,mcPVS-Net的平均DICE系数为0.80,平均精度为0.81,Recall为0.79,表明具有良好的特异性和敏感性。分割的PVS体积与基底神经节(r=0.541,p<0.001)和白质区域(r=0.706,p<0.001)的视觉评分显着相关,和PVS体积在视觉评分不同的受试者之间存在显着差异。不同的扫描仪供应商之间的细分性能是一致的。PVS量与年龄显著相关,高血压,WMH。在联合成像扫描仪数据集中,PVS体积与在T1w或T2w图像上评估的PVS视觉评分显示出良好的关联。与以前发布的方法相比,mcPVS-Net显示出更高的准确性,并改善了基底神经节区域的PVS分割。
结论:mcPVS-Net显示了从3DT1w图像中分割PVS的良好准确性。它可以作为未来PVS研究的有用工具。
BACKGROUND: Perivascular spaces (PVS) visible on magnetic resonance imaging (MRI) are significant markers associated with various neurological diseases. Although quantitative analysis of PVS may enhance sensitivity and improve consistency across studies, the field lacks a universally validated method for analyzing images from multi-center studies.
METHODS: We annotated PVS on multi-center 3D T1-weighted (T1w) images acquired using scanners from three major vendors (Siemens, General Electric, and Philips). A neural network, mcPVS-Net (multi-center PVS segmentation network), was trained using data from 40 subjects and then tested in a separate cohort of 15 subjects. We assessed segmentation accuracy against ground truth masks tailored for each scanner vendor. Additionally, we evaluated the agreement between segmented PVS volumes and visual scores for each scanner. We also explored correlations between PVS volumes and various clinical factors such as age, hypertension, and white matter hyperintensities (WMH) in a larger sample of 1020 subjects. Furthermore, mcPVS-Net was applied to a new dataset comprising both T1w and T2-weighted (T2w) images from a United Imaging scanner to investigate if PVS volumes could discriminate between subjects with differing visual scores. We also compared the mcPVS-Net with a previously published method that segments PVS from T1 images.
RESULTS: In the test dataset, mcPVS-Net achieved a mean DICE coefficient of 0.80, with an average Precision of 0.81 and Recall of 0.79, indicating good specificity and sensitivity. The segmented PVS volumes were significantly associated with visual scores in both the basal ganglia (r = 0.541, p < 0.001) and white matter regions (r = 0.706, p < 0.001), and PVS volumes were significantly different among subjects with varying visual scores. Segmentation performance was consistent across different scanner vendors. PVS volumes exhibited significant associations with age, hypertension, and WMH. In the United Imaging scanner dataset, PVS volumes showed good associations with PVS visual scores evaluated on either T1w or T2w images. Compared to a previously published method, mcPVS-Net showed a higher accuracy and improved PVS segmentation in the basal ganglia region.
CONCLUSIONS: The mcPVS-Net demonstrated good accuracy for segmenting PVS from 3D T1w images. It may serve as a useful tool for future PVS research.