Fig 1.
A schematic diagram of the deep learning-based WMH segmentation algorithm.
The deep learning-based WMH segmentation algorithm is consisting of two independent processes. First, brain extraction is conducted with two rigid transformation and in-house brain extraction algorithm using 3D U-Net. Second, in-house convolutional neural networks segment WMH from the preprocessed brain parenchyma image.
Fig 2.
Three pairs of a pre-processed FLAIR image and a mask of segmented WMH with total WMH volume of each case in different Fazekas categories (a-c) (WMH volume: a, 4.11 mL; b, 20.59 mL; c, 47.69 mL, the ratio of WMH volume / total white matter volume: a, 1.09%; b, 5.18%; c, 9.94%). The last pair is the images of a subcortical vascular dementia patient (c). FLAIR, fluid-attenuated inversion recovery.
Fig 3.
Flow diagram showing the selection process of patients and their Fazekas scale.
FLAIR, fluid-attenuated inversion recovery; MRI, magnetic resonance imaging.
Table 1.
Characteristics of patients based on the Fazekas scale.
Fig 4.
WMH volume (a) and WMH volume ratio (b) for Fazekas categories.
Table 2.
Diagnostic performance of WMH volume for differentiating Fazekas scale with its cut-off values.