AIM:　To　explore　the　application　value　of　deep　learning　technology　in　automatic　meibomian　glands　segmentation.　METHODS:　Infrared　meibomian　gland　images　were　collected　and　193　of　them　were　picked　out　for　establishing　the　database.　The　images　were　manually　labeled　by　three　clinicians.　UNet++　network　and　automatic　data　expansion　strategy　were　introduced　to　construct　the　automatic　meibomian　glands　segmentation　model.　The　feasibility　and　effectiveness　of　the　proposed　segmentation　model　were　analyzed　by　precision,　sensitivity,　specificity,　accuracy　and　intersection　over　union.　RESULTS:　Taking　manual　labeling　as　the　gold　standard,　the　presented　method　segment　the　glands　effectively　and　steadily　with　accuracy,　sensitivity　and　specificity　of　94.31%,　82.15%　and　96.13%　respectively.　On　the　average,　only　0.11s　was　taken　for　glands　segmentation　of　single　image.　CONCLUSIONS:　In　this　paper,　deep　learning　technology　is　introduced　to　realize　automatic　segmentation　of　meibomian　glands,　achieving　high　accuracy,　good　stability　and　efficiency.　It　would　be　quite　useful　for　calculation　of　gland　morphological　parameters,　the　clinical　diagnosis　and　screening　of　related　diseases,　improving　the　diagnostic　efficiency.　©　2022　International　Journal　of　Ophthalmology　(c/o　Editorial　Office).　All　rights　reserved.