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