A Nested Attention Guided UNet++ Architecture for White Matter Hyperintensity Segmentation

White Matter Hyperintensity (WMH) is a common finding in Magnetic Resonance Imaging (MRI) of patients with cerebral infarction and is associated with poor prognosis. Accurate and rapid segmentation of WMH lesions is critical for clinicians to assess the risk of rebleeding and the long-term prognosis of thrombolytic patients. However, segmentation can be challenging due to the erratic signals of WMH in MRI, leading to imprecise results. Deep learning-based approaches have been proposed, but the dice similarity coefficient remains low. Atlas images are navigation maps that integrate various medical information expressions. In this study, we propose a nested attention-guided UNet++ framework that employs attention mechanisms to capture local and global features of WMH lesions using atlas images for segmentation. The framework consists of two modules, the atlas attention module, and the nested attention-guided nested U-Net module. The atlas attention module generates the atlas attention map, which is used as the input for the nested attention-guided nested U-Net module that generates the segmentation map of the FLAIR image. Experimental results demonstrate that the proposed NAUNet++ framework converges faster than conventional UNet and UNet++ approaches. Moreover, the nested architecture enhances recall and f1 scores of the segmentation results compared to the attention-guided approach.