Towards the automatic computational assessment of enlarged perivascular spaces on brain magnetic resonance images: A systematic review

Abstract

Enlarged perivascular spaces (EPVS), visible in brain MRI, are an important marker of small vessel disease and neuroinflammation. We systematically evaluated the literature up to June 2012 on possible methods for their computational assessment and analyzed confounds with lacunes and small white matter hyperintensities. We found six studies that assessed/identified EPVS computationally by seven different methods, and four studies that described techniques to automatically segment similar structures and are potentially suitable for EPVS segmentation. T2-weighted MRI was the only sequence that identified all EPVS, but FLAIR and T1-weighted images were useful in their differentiation. Inconsistency within the literature regarding their diameter and terminology, and overlap in shape, intensity, location, and size with lacunes, conspires against their differentiation and the accuracy and reproducibility of any computational segmentation technique. The most promising approach will need to combine various MR sequences and consider all these features for accurate EPVS determination.

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

“…From this subset of slices, the slice where our classifier operated was selected after applying contrast-limited adaptive histogram equalization (CLAHE) [31] to the polygonal regions, thresholding them to 0.43 times the maximum intensity level [11,12] (Figure 3D), and counting the number of thresholded hyperintense regions on each candidate slice with area between 3 and 15 times the in-plane voxel dimensions [12]. Although this procedure overestimates the number of PVS in the presence of other features of SVD markers (e.g.…”

“…Although this procedure overestimates the number of PVS in the presence of other features of SVD markers (e.g. small lesions and lacunes) [11], it provides a good estimate of the number of PVS on each candidate axial slice, so as to select the one with more PVS. …”

“…Therefore, PVS can be potentially quantified. Visual counting and/or manual delineation of PVS can be time-consuming, and the development of computational methods to assess them is challenging, partly due to inconsistencies within the literature regarding PVS diameter and overlap in shape, intensity, location and size with these of lacunes [11]. Recently, [12] and [13] presented computational methods to obtain quantificative measurements of PVS and validated the usefulness of their procedures in clinical research, but both approaches are semi-automatic being, therefore, prone to interobserver variations and could be time-consuming.…”

Quantitative MRI of Perivascular Spaces at 3T for Early Diagnosis of Mild Cognitive Impairment

“…19,20 Owing to the difficulties involved in their quantification, EPVS are a relatively under-studied biomarker. 21 Current evaluation of their properties, such as their shape, size, and numbers, remains a subjective process. 22 Development of objective methods for quantifying these properties is, thus, highly desirable with a significant potential for clinical utility.…”

Developing an Integrated Image Bank and Metadata for Large-scale Research in Cerebrovascular Disease: Our Experience from the Stroke Image Bank Project

“…From image analysis perspective, well-characterized images with detailed metadata are increasingly needed for studies that typically need larger samples or more variety of cases than are available in individual studies-these include studies to develop machine learning methods for image analysis, in stratified medicine, and large studies of genetics, e.g., genome wide association studies where typically many thousands of cases are needed (Hernández et al, 2013;Caligiuri et al, 2015). The availability of large amount of data could help develop models that can be generalizable based on the patterns the underlying algorithms are able to "learn" from the data.…”