Multi-class　segmentation　of　eye　images　plays　a　pivotal　role　in　assessing　patients　with　myasthenia　gravis,　and　the　measurement　results　rely　heavily　on　the　segmentation　accuracy.　However,　there　is　still　a　problem　with　inaccurate　boundary　segmentation.　Compared　to　heuristic-based　network　structure　optimization,　exploring　effective　loss　function　is　an　intuitive,　simple,　and　interpretable　way　to　address　this　issue.　In　this　paper,　we　experimentally　verify　the　effectiveness　of　boundary　loss　for　multi-class　segmentation　of　eye　images　and　investigate　its　hybrid　law　with　other　segmentation　losses.　The　application　of　the　study　significantly　enhances　the　accuracy　of　myasthenia　gravis　scoring　and　holds　promise　for　assisting　in　the　evaluation　of　various　other　eye　diseases.　　©　2024　IEEE.