A Computer-Aided System for Ocular Myasthenia Gravis Diagnosis

• 作者: Guanjie Liu Yan Wei Yunshen Xie Jianqiang Li Liyan Qiao Ji-jiang Yang
• 来源期刊: 清华大学学报自然科学版（英文版）
• 年，卷（期）: 2021,（5）
• 所属期刊栏目: REGULAR ARTICLES
• 页码范围: 749-758
• 页数: 10页
• 语种: 英文
• DOI: 10.26599/TST.2021.9010025

摘要

The current mode of clinical aided diagnosis of Ocular Myasthenia Gravis(OMG)is time-consuming and laborious,and it lacks quantitative standards.An aided diagnostic system for OMG is proposed to solve this problem.The values calculated by the system include three clinical indicators:eyelid distance,sclera distance,and palpebra superior fatigability test time.For the first two indicators,the semantic segmentation method was used to extract the pathological features of the patient's eye image and a semantic segmentation model was constructed.The patient eye image was divided into three regions:iris,sclera,and background.The indicators were calculated based on the position of the pixels in the segmentation mask.For the last indicator,a calculation method based on the Eyelid Aspect Ratio(EAR)is proposed;this method can better reflect the change of eyelid distance over time.The system was evaluated based on the collected patient data.The results show that the segmentation model achieves a mean Intersection-Over-Union(mloU)value of 86.05％.The paired-sample T-test was used to compare the results obtained by the system and doctors,and the p values were all greater than 0.05.Thus,the system can reduce the cost of clinical diagnosis and has high application value.