The clinical activity score （CAS） is one of the important assessment methods for clinical diagnosis of thyroid associated ophthalmopathy （TAO） disease. Manual diagnosis of TAO is susceptible to the subjective experience of ophthalmologists due to the diversity of TAO symptoms and the influence of non-diseased areas. The accurate acquisition of key facial areas of TAO patients is one of the significant prerequisites for early diagnosis of TAO. Therefore， this paper proposes a lightweight algorithm for automatic segmentation of TAO diseased areas based on DSE-Net. The DSE-Net adopts U-Net as the backbone model， and the dense squeeze-and-excitation （DSE） channel attention module， which is designed to extract low-level features of the encoding structure layer by layer and fuse high-level features of the decoding structure layer， further enhances the feature extraction capability of the model. Tests on the sclera， eyelid， and lacrimal caruncle datasets demonstrate the effectiveness of DSE-Net， with Dice coefficients reaching 84.8%， 84.7%， and 92.7%， and IoUs reaching 74.0%， 74.7%， and 86.5%， respectively. The superiority of DSE-Net is also proved by a large number of comparative experiments. The proposed model has fewer parameters， simple structure and strong feature extraction ability， providing significant information for the early diagnosis and prognosis treatment of TAO.