Research And Application Of Deep Learning On Fundus Image Analysis

The retinal vascular network is the only vascular system that can be v isualized and photographed in vivo.Retinal vascular imaging can provide clinical prognostic information for patients with specific cardiovascular and ophthalmic diseases.Segmentation of retinal blood vessels is a prerequisite for monitoring the condition of the retinal blood vessel network.Glaucoma is a widespread eye disease with a high incidence,which leads to decreased vision.Because the deterioration of this disease is irreversible,early and timely diagnosis is very important.Cup to disc ratio(CDR)is one of the most common biomarkers used to diagnose glaucoma.Therefore,accurate segmentation of the optic disc optic disc and fundus vessels and their morphological analysis have important application value and practical significance.Before segmenting the fundus image,this paper first preprocesses the original fundus image.Including image channel selection,filter denoising,blood vessel quality assessment,etc.After pre-processing,deep learning network was used to realize blood vessel segmentation,optic disc segmentation and optic cup segmentation of fundus image.After the segmentation is completed,the morphological analysis of each structure is performed.The specific research contents are as follows:(1)We have proposed and verified an unsupervised vascular quality assessment scheme for image screening in pretreatment.Before the segmentation task,it can effectively remove the pictures that have an adverse effect on the accuracy of the algorithm,and at the same time can provide a diagnosis basis for the diagnosis of certain diseases.(2)A novel framework for joint segmentation of OD and OC is proposed.The main contribution of our work is: In order to eliminate uncertainty,we learn from the maximum likelihood estimation(MLE)of traditional Bayesian neural networks(BNN),and adopt novel frameworks(including segmented networks and uncertainties)To realize the estimation network.Combined with the training strategy of transfer learning,the segmentation accuracy is improved while accelerating the network convergence speed.(3)Apply the above network to blood vessel segmentation.Vessel segmentation utilizes separable space and channel flow and dense adjacent vessel prediction to capture the maximum spatial correlation between vessels.I n the segmentation of optic disc and optic cup,both geometric transformations and Overlapped patches are used in the training and prediction stages to effectively use the information learned in the training stage and refine the segmentation.(4)A morphological analysis method is proposed based on the above segmentation results.According to the blood vessel segmentation results,the diameter,length,density,etc.of the blood vessel can be accurately calculated,the most important of which is the calculation of the blood vessel diameter.We propose a method for measuring the diameter of blood vessels based on the center line of the blood vessels.For the segmentation results of the optic disc optic disc,data such as the ratio of the optic disc to the optic disc(the ratio of the diameter of the optic cup to the optic disc)can be accurately calculated.Finally,in this paper,on three public databases DRIVE,REVIEW and ORIGA,the above research contents were tested and compared with the existing research results.The results show that the blood vessel segmentation and optic disc segmentation methods proposed in this paper have high accuracy and stability,while the morphological analysis of blood vessels and optic disc optic discs have high accuracy.