Meibomian　gland　imaging　technology　is　widely　used　in　the　classification　diagnosis,　management　and　personalized　treatment　of　dry　eye　syndrome.　Direct　observation　and　qualitative　evaluation　by　ophthalmologists　may　result　in　low　subjective　and　reproducible　evaluation.　To　improve　the　diagnostic　efficiency　of　ophthalmologists,　researchers　have　proposed　a　series　of　gland　segmentation　method　for　infrared　meibomian　gland　images　based　on　U-Net.　However,　the　segmentation　results　are　still　not　ideal　at　image　edges,　at　locations　of　reflective　points,　and　in　areas　with　dense　glandular　structures.　Considering　the　characteristics　of　infrared　meibomian　gland　imaging　and　glandular　distribution,　this　paper　proposed　a　glandular　segmentation　model　SS-UNet　based　on　shape　stream　and　multi-scale　feature　fusion,　introduced　an　atrous　convolution　module　to　enhance　the　model＇s　feature　extraction　ability,　designed　a　shape　stream　auxiliary　branch　to　fully　learn　the　shape　information　of　glands,　used　a　multi-scale　feature　fusion　module　to　obtain　feature　representations　of　glands　with　different　thicknesses.　To　verify　the　validity　of　the　model,　a　fully　annotated　dataset　containing　203　infrared　meibormian　gland　images　were　collected　by　the　ophthalmology　department　of　Fujian　Provincial　Hospital　and　used　to　conduct　comparative　experiments　with　other　advanced　medical　segmentation　models　in　the　same　experimental　environment,　perform　module　ablation　analysis,　and　display　the　visualization　results.　The　experimental　results　showed　that　the　Ace,　Dice,　loll　indicators　of　SS-UNet　reached　94.62%,　80.94%,　and　68.17%,　respectively,　which　were　improved　by　0.36%,　1.41%,　and　1.95%　compared　to　the　benchmark　network　U-Net,　significantly　improving　the　gland　segmentation　results.　This　work　has　shown　that　SS-UNet　was　able　to　fully　utilize　information　such　as　the　shape　and　scale　of　glands　to　solve　incorrect　segmentation　problems　such　as　glandular　adhesions　and　missed　detections,　effectively　improving　segmentation　accuracy　and　providing　objective　basis　for　assisting　clinical　diagnosis.　©　2025　Chinese　Academy　of　Medical　Sciences.　All　rights　reserved.