Semisupervised white matter hyperintensities segmentation on MRI

This study proposed a semisupervised loss function named level‐set loss (LSLoss) for cerebral white matter hyperintensities (WMHs) segmentation on fluid‐attenuated inversion recovery images. The training procedure did not require manually labeled WMH masks. Our image preprocessing steps included biased field correction, skull stripping, and white matter segmentation. With the proposed LSLoss, we trained a V‐Net using the MRI images from both local and public databases. Local databases were the small vessel disease cohort (HKU‐SVD, n = 360) and the multiple sclerosis cohort (HKU‐MS, n = 20) from our institutional imaging center. Public databases were the Medical Image Computing Computer‐assisted Intervention (MICCAI) WMH challenge database (MICCAI‐WMH, n = 60) and the normal control cohort of the Alzheimer's Disease Neuroimaging Initiative database (ADNI‐CN, n = 15). We achieved an overall dice similarity coefficient (DSC) of 0.81 on the HKU‐SVD testing set (n = 20), DSC = 0.77 on the HKU‐MS testing set (n = 5), and DSC = 0.78 on MICCAI‐WMH testing set (n = 30). The segmentation results obtained by our semisupervised V‐Net were comparable with the supervised methods and outperformed the unsupervised methods in the literature. This article presented a study of training a white matter hyperintensity segmentation network on T2‐weighted fluid‐attenuated inversion recovery images without using manual labeled data. The segmentation performance outperform than other semisupervised and unsupervised methods.