Automatic Rating of Perivascular Spaces in Brain MRI Using Bag of Visual Words

Perivascular spaces and their associations with risk factors, clinical disorders and neuroimaging features: A systematic review and meta-analysis

BACKGROUND

Perivascular spaces, visible on brain magnetic resonance imaging, are thought to be associated with small vessel disease, neuroinflammation, and to be important for cerebral hemodynamics and interstitial fluid drainage.

AIMS

To benchmark current knowledge on perivascular spaces associations with risk factors, neurological disorders, and neuroimaging lesions, using systematic review and meta-analysis.

SUMMARY OF REVIEW

We searched three databases for perivascular spaces publications, calculated odds ratios with 95% confidence interval and performed meta-analyses to assess adjusted associations with perivascular spaces. We identified 116 relevant studies (n = 36,108) but only 23 (n = 12,725) were meta-analyzable. Perivascular spaces assessment, imaging and clinical definitions varied. Perivascular spaces were associated (n; OR, 95%CI, p) with ageing (8395; 1.47, 1.28-1.69, p = 0.00001), hypertension (7872; 1.67, 1.20-2.31, p = 0.002), lacunes (4894; 3.56, 1.39-9.14, p = 0.008), microbleeds (5015; 2.26, 1.04-4.90, p = 0.04) but not WMH (4974; 1.54, 0.71-3.32, p = 0.27), stroke or cognitive impairment. There was between-study heterogeneity. Lack of appropriate data on other brain disorders and demographic features such as ethnicity precluded analysis.

CONCLUSIONS

Despite many studies, more are required to determine potential pathophysiological perivascular spaces involvement in cerebrovascular, neurodegenerative and neuroinflammatory disorders.

Reliability of an automatic classifier for brain enlarged perivascular spaces burden and comparison with human performance

In the brain, enlarged perivascular spaces (PVS) relate to cerebral small vessel disease (SVD), poor cognition, inflammation and hypertension. We propose a fully automatic scheme that uses a support vector machine (SVM) to classify the burden of PVS in the basal ganglia (BG) region as low or high. We assess the performance of three different types of descriptors extracted from the BG region in T2-weighted MRI images: (i) statistics obtained from Wavelet transform's coefficients, (ii) local binary patterns and (iii) bag of visual words (BoW) based descriptors characterizing local keypoints obtained from a dense grid with the scale-invariant feature transform (SIFT) characteristics. When the latter were used, the SVM classifier achieved the best accuracy (81.16%). The output from the classifier using the BoW descriptors was compared with visual ratings done by an experienced neuroradiologist (Observer 1) and by a trained image analyst (Observer 2). The agreement and cross-correlation between the classifier and Observer 2 (κ = 0.67 (0.58-0.76)) were slightly higher than between the classifier and Observer 1 (κ = 0.62 (0.53-0.72)) and comparable between both the observers (κ = 0.68 (0.61-0.75)). Finally, three logistic regression models using clinical variables as independent variable and each of the PVS ratings as dependent variable were built to assess how clinically meaningful were the predictions of the classifier. The goodness-of-fit of the model for the classifier was good (area under the curve (AUC) values: 0.93 (model 1), 0.90 (model 2) and 0.92 (model 3)) and slightly better (i.e. AUC values: 0.02 units higher) than that of the model for Observer 2. These results suggest that, although it can be improved, an automatic classifier to assess PVS burden from brain MRI can provide clinically meaningful results close to those from a trained observer.