Computer-Aided Extraction of Select MRI Markers of Cerebral Small Vessel Disease: A Systematic Review
It addresses the need for objective and efficient extraction of imaging biomarkers for cerebral small vessel disease, which contributes to cognitive impairment in aging, but is incremental as it reviews existing methods without proposing new ones.
This systematic review summarized computer-aided methods for extracting MRI biomarkers of cerebral small vessel disease, finding that while good performance metrics were achieved in local datasets, no generalizable pipelines have been validated across different cohorts.
Cerebral small vessel disease (CSVD) is a major vascular contributor to cognitive impairment in ageing, including dementias. Imaging remains the most promising method for in vivo studies of CSVD. To replace the subjective and laborious visual rating approaches, emerging studies have applied state-of-the-art artificial intelligence to extract imaging biomarkers of CSVD from MRI scans. We aimed to summarise published computer-aided methods to examine three imaging biomarkers of CSVD, namely cerebral microbleeds (CMB), dilated perivascular spaces (PVS), and lacunes of presumed vascular origin. Seventy-one classical image processing, classical machine learning, and deep learning studies were identified. CMB and PVS have been better studied, compared to lacunes. While good performance metrics have been achieved in local test datasets, there have not been generalisable pipelines validated in different research or clinical cohorts. Transfer learning and weak supervision techniques have been applied to accommodate the limitations in training data. Future studies could consider pooling data from multiple sources to increase diversity, and validating the performance of the methods using both image processing metrics and associations with clinical measures.