Automated White Matter Segmentation in MR Images Using Residual UNet

Abstract

Vascular changes in small vessels can be observed through Flair MR images called white matter hyperintensities (WMHs). WMHs are associated with cerebral small vessel disease, aging, dementia, and stroke. Quantification of WHMs is important for diagnosis, prognosis, monitoring of patients, and research studies. Manual segmentation of WHMs is a time-consuming task and is subjective to the observer. That's why we need an automated segmentation method. WHMs' segmentation is a challenging task due to their heterogeneous characteristics and coexistence with other similar-appearing structures. We proposed an architecture that uses residual blocks in a U-Net-based network to segment the WHMs from T1-weighted, Flair images of brains. In this study, we used the MICCIA White Matter Hyperintensities Challenge 2017 Dataset to train and test the proposed model. We evaluated the proposed method using the standard MWM challenge in 2017 evaluation measures and achieved better results than the state-of-the-art technique. [1]