Robustness of Probabilistic U-Net for Automated Segmentation of White Matter Hyperintensities in Different Datasets of Brain MRI

White Matter Hyperintensities (WMHs) are neu-roradiological features often seen in T2-FLAIR brain MRI as white regions (i.e., hyperintensities) and characteristic of small vessel disease (SVD). Detailed measurements of WMHs (e.g., their volumes, locations, distributions) are vital for clinical research, but segmenting WMHs is challenging due to WMHs' ill-posed boundaries. In this study, we investigate the robustness of Probabilistic U-Net and other deterministic deep learning models (i.e., U-Net and its variations) for automatic segmentation of WMHs. In particular, we are interested in the robustness of U-Net based deep learning models, especially the Probabilistic U-Net, for segmenting WMHs in brain MRI from different datasets. Thus, we performed two different experiments, which are k- fold cross validation experiment (i.e., training and testing using the same dataset) and cross dataset experiment (i.e., testing in different dataset). Based on our experiments, Probabilistic U-Net outperformed other tested models in k-fold cross validation experiment. On the other hand, we found that Probabilistic U-Net captured different types of uncertainty when tested in different dataset.