This　paper　presents　an　eye　image　segmentation-based　computer-aided　system　for　automatic　diagnosis　of　ocular　myasthenia　gravis　(OMG),　called　OMGMed.　It　provides　great　potential　to　effectively　liberate　the　diagnostic　efficiency　of　expert　doctors　(the　scarce　resources)　and　reduces　the　cost　of　healthcare　treatment　for　diagnosed　patients,　making　it　possible　to　disseminate　high-quality　myasthenia　gravis　healthcare　to　under-developed　areas.　The　system　is　composed　of　data　pre-processing,　indicator　calculation,　and　automatic　OMG　scoring.　Building　upon　this　framework,　an　empirical　study　on　the　eye　segmentation　algorithm　is　conducted.　It　further　optimizes　the　algorithm　from　the　perspectives　of　＂network　structure＂　and　＂loss　function＂,　and　experimentally　verifies　the　effectiveness　of　the　hybrid　loss　function.　The　results　show　that　the　combination　of　＂nnUNet＂　network　structure　and　＂Cross-Entropy　+　Iou　+　Boundary＂　hybrid　loss　function　can　achieve　the　best　segmentation　performance,　and　its　MIOU　on　the　public　and　private　myasthenia　gravis　datasets　reaches　82.1%　and　83.7%,　respectively.　The　research　has　been　used　in　expert　centers.　The　pilot　study　demonstrates　that　our　research　on　eye　image　segmentation　for　OMG　diagnosis　is　very　helpful　in　improving　the　healthcare　quality　of　expert　doctors.　We　believe　that　this　work　can　serve　as　an　important　reference　for　the　development　of　a　similar　auxiliary　diagnosis　system　and　contribute　to　the　healthy　development　of　proactive　healthcare　services.