Purpose

White matter hyperintensities of presumed vascular origin (WMH), a hallmark feature of cerebral small vessel disease (CSVD), are FLAIR/T2-hyperintense lesions that predict various clinical readouts, e.g., stroke or dementia [6, 8].WMH are commonly determined according to their volume load [1], while capturing or classifying more subtle features such as different WMH patterns or “WMH mimics” is demanding.We aimed to compare automatic methods to segment challenging WMH (e.g. multifocal spots, peri-basal ganglia WMH or WMH “mimics” surrounding lacunes/large hemorrhages) in clinical CSVD cases.

Materials & Methods

We applied seven different automatic WMH segmenting methods (LGA and LPA [7], SLS [5], MDbrain (Mediaire GmbH), BIANCA [2], FreeSurfer[4] and PGS – a Deep Learning approach [3]) in T1/FLAIR MRI sequences and compared their performance against gold standard manual segmentations in 10 CSVD patients with challenging WMH aiming to identify the most suitable method to segment them. Segmentation accuracy was determined through Dice similarity coefficient and other metrics measuring sensitivity or precision.

Results

In our dataset, the PGS (DSC:0.6), LPA (DSC:0.59) and MDbrain (DSC:0.57) were superior in detecting periventricular and deep “multifocal spot WMH challenges” with a high sensitivity and precision rate. However, similarly to other methods, on a variable scale, they falsely segmented “WMH mimics” surrounding CSVD-related lesions as “true WMH” resulting in an overestimation of WMH volume.The volume of these “WMH mimics” segmentations is fluctuate on the method, but PGS performs better as it detects less false-positive-WMH-volume compared to other tools.