Automatic layer segmentation of optical coherence tomography images in retinal vascular diseases
10.3969/j.issn.1674-8115.2019.06.009
- Author:
Yu-Peng XU
1
Author Information
1. Shanghai Engineering Center for Visual Science and Photomedicine, Shanghai Key Laboratory of Fundus Disease, Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine
- Publication Type:Journal Article
- Keywords:
Automatic analysis algorithm;
Computer vision;
Machine learning;
Optical coherence tomography (OCT);
Retinal vascular diseases
- From:
Journal of Shanghai Jiaotong University(Medical Science)
2019;39(6):613-621
- CountryChina
- Language:Chinese
-
Abstract:
Objective • To explore the layer segmentation method of optical coherence tomography (OCT) images of retinal vascular diseases using an unsupervised learning method, and compare it with the built-in layering method of OCT machine. Methods • Standardized image acquisition was performed on OCT images from 50 patients with myopic choroidal neovascularization (mCNV) and 20 patients with diabetic macular edema (DME). Standards were established by manual marking of hierarchical information by professional physicians. A retinal multi-layer segmentation method based on the minimization of interlayer energy was proposed, and the results were compared with those obtained by the built-in layering method of OCT machine. The layering accuracy was verified by the unmarked boundary position error. Results • This segmentation method divided the retina of each patient into five layers: internal limiting membrane, lower layer of nerve fiber layer, upper layer of outer nuclear layer, upper layer of ellipsoid zone and Bruch's membrane. The average segmentation error in the overall data set was (4.831±7.015) μm. The error of mCNV group and DME group were (4.839±16.819) μm and (5.048±9.986) μm, respectively, both of which were lower than the automatic measurement results of OCT machine [(13.638±58.024) μm and (14.796±45.342) μm, respectively]. The accuracy of this method at each layer was higher than that of the automatic measurement. Conclusion • This multi-layer segmentation method can be used for segmentation of different types of retinal vascular diseases, and the results are significantly better than those obtained by the built-in method in OCT machine. It can be extended for layer segmentation of other retinal vascular diseases.
