基于深度学习的眼角膜图像自动化分析研究

Research on Automated Corneal Image Analysis Based on Deep Learning

为使本科生了解生物医学影像处理技术,掌握深度学习方法,结合吉林大学大学生创新创业训练计划,设计了"基于深度学习的眼角膜图像自动化分析研究"实验项目。在研发治疗角膜新生血管(CNV:Corneal Neovascularization)等疾病的药物过程中,人们需要观察并获取小鼠眼角膜血管在药物影响下的生长情况及数据。为此设计了基于深度学习的眼角膜图像自动化分析程序,以合作医院提供的经凝胶处理的小鼠眼角膜图像为项目研究对象,通过Matlab工具以及神经网络等深度学习算法完成对眼角膜特征的提取和分割。采用SegNet语义分割网络和基于SVM(Support Vector Machine)的图像分割两种方法实现小鼠眼角膜图像的自动提取,分析了两种方法下眼角膜提取的精度与可靠性。结果表明,使用SegNet语义分割网络得到的结果精度较高,其准确率可以达到97.75%。

In order to let the undergraduates understand biomedical image processing technology and master deep learning methods,combined with the innovation and entrepreneurship training program of Jilin University college students,the experimental project“Research on automated analysis of corneal images based on deep learning”is completed.To assist the medical research and development of effective drugs for the treatment of CNV(Corneal Neovascularization)diseases,it is necessary to observe and obtain data on the growth of mouse corneal blood vessels under the influence of drugs.Therefore,an automated corneal image analysis program based on deep learning is designed,where the gel-processed mouse corneal image provided by the cooperative hospital is used as the research object,and the corneal features are completed through MATLAB tools and deep learning algorithms such as neural networks.SegNet semantic segmentation network and SVM(Support Vector Machine)-based image segmentation are used to achieve automatic extraction of mouse corneal images.The accuracy and reliability of corneal extraction under the two methods are analyzed.The results show that the use of SegNet semantic segmentation network is high in accuracy,and its accuracy rate can reach 97.75%.