Abstract: Abstract： White matter hyperintensities， commonly seen in the image of cerebral small vessel disease （CSVD）， shed light on the clinical diagnoses of patients with cerebral small vessel disease. White matter hyperintensities segmentation， as a basic work in clinical diagnosis， often requires experienced doctors to carry it out manually， which is time-consuming and intricate. White matter hyperintensities， referring to the hyperintense shadows in T2 weighted magnetic resonance images of the brains or fluid-attenuated inversion recovery sequence images， are of higher gray values than other brain tissues. To enhance the attention to areas of white matter hyperintensities， this paper proposes a network model of a high gray value attention mechanism in light of the imaging characteristics of white matter hyperintensities. The model， based on the UNet， introduces a module of high gray value attention so that it can pay more attention to the areas of relatively high gray values in the images. It also introduces a residual mixed attention module to enhance the ability for extracting features of the net model. As a result， it significantly enhances the segmentation effect of white matter hyperintensities， with its DSC and Recall indicators reaching 0.8330 and 0.8870， respectively， which is better than existing algorithms. Moreover， ablation experiments verified the effectiveness of the high gray value attention module and the residual hybrid attention module. This paper provides a new method for the FLAIR-based segmentation of white matter hyperintensities lesion， and verifies the feasibility of combining the traditional method for image segmentation with in-depth learning technology.

Key words: Key words： white matter hyperintensities, deep learning, medical image segmentation, UNet, high gray value attention mechanism