White Matter Hyperintensities Segmentation Using Probabilistic TransUNet

White Matter Hyperintensities (WMH) are areas of the brain that have a higher intensity than other normal brain regions on Magnetic Resonance Imaging (MRI) scans. WMH is often associated with small vessel disease in the brain, making early detection of WMH important. However, there are two common issues in detecting WMH: high ambiguity and difficulty detecting small WMH. In this study, we propose a method called Probabilistic TransUNet to address the precision of small object segmentation and the high ambiguity of medical images. We conducted a k-fold cross-validation and cross-dataset robustness experiment to measure model performance. Based on the experiments, Transformer-based models (TransUNet and Probabilistic TransUNet) were found to provide more precise and better segmentation results, as demonstrated by the higher DSC scores obtained compared to CNN-based models (UNet and Probabilistic UNet) and their ability to segment small WMH objects. The addition of a probabilistic model and the use of a transformer-based approach was able to achieve better performance. Our proposed method achieved a DSC score of 0.742 in K-fold cross-validation outperforming the previous method. The use of CNN and Transformer-based probabilistic models increased the average DSC score by +0.016 and +0.001, respectively. In cross-dataset robustness, our proposed method achieved the second-best result with a DSC score of 0.605