Meibomian　gland　dysfunction　(MGD)　is　the　most　common　cause　of　dry　eye　disease.　Ophthalmologists　conduct　qualitative　evaluation　of　meibomian　glands(MGs)　of　patients　by　observing　infrared　meibomian　gland　images.　But　it　is　subjective　to　make　a　diagnosis　only　with　the　naked　eye.　Automatic　segmentation　of　MGs　could　be　challenging　and　play　a　key　role　in　MGD　morphology　analysis　and　diagnosis.　In　this　paper,　an　automatic　gland　segmentation　method　based　on　UNet++　and　a　meibography　image　dataset　are　proposed.　Data　augmentation　is　used　to　expand　training　samples.　Infrared　meibomian　gland　images　are　fed　into　the　preserved　model　for　accurate　segmentation.　The　experiments　including　comparison　with　the　latest　methods　show　that　the　presented　method　effectively　segment　the　MGs　and　outperform　other　methods　with　an　average　accuracy　of　94.28%.　©　2021　IEEE.