Myasthenia　Gravis　(MG)　is　an　autoimmune　neuromuscular　disorder　that　heavily　affects　various　daily　actions　of　the　suffers.　The　manifestations　of　MG　disease　are　fluctuating　muscle　weakness　and　fatigue　across　different　muscle　units　of　human　body,　while　ocular　muscles　involvement　is　regarded　as　primary　symptom　in　a　majority　of　the　patients.　Currently,　the　conventional　clinical　approach　for　MG　assessment　involves　identifying　and　measuring　key　ocular　structures　manually,　which　is　resource-intensive　and　prone　to　subjective　errors.　In　our　paper,　a　novel　application　of　computer　vision　technique　is　introduced　to　assist　in　the　quantitative　evaluation　of　extraocular　muscles　in　MG　patients.　By　employing　advanced　segmentation　algorithms,　both　prior　feature-driven　and　deep　feature-driven　methods,　we　aims　to　automatically　determine　the　ocular　structures　such　as　the　iris,　sclera,　and　pupil,　etc.　This　pilot　study　serves　as　a　proof　of　concept　for　using　classical　segmentation　models　to　facilitate　automatic　and　non-invasive　MG　assessments,　enhancing　clinical　management　and　patient　autonomy　in　monitoring　their　condition.　Our　approach　not　only　aids　physicians　in　obtaining　more　accurate　measurements　for　better　disease　management　but　also　reduces　the　necessity　for　frequent　clinical　visits,　potentially　improving　adherence　to　treatment　and　overall　quality　of　life　for　MG　patients.　　©　2024　IEEE.