Automated retinal layer segmentation in OCT images of age‐related macular degeneration. Issue 11 (1st September 2019)

• Record Type: Journal Article
• Authors: Chen, Zailiang; Li, Dabao; Shen, Hailan; Mo, Yufang; Wei, Hao; Ouyang, Pingbo
• Is Part Of: IET image processing. Volume 13:Issue 11(2019)
• Page Start: 1824
• Page End: 1834
• Publication Date: 2019-09-01
• Subjects: image segmentation -- medical image processing -- optical tomography -- diseases -- eye -- vision defects -- biomedical optical imaging -- dynamic programming -- graph theory -- feature extraction; automated retinal layer segmentation -- OCT images -- age‐related macular degeneration -- common eye disease -- progressive degeneration -- abundant drusen -- optical coherence tomography -- physiological structure -- retinal epithelium -- drusen quantification -- automatic method -- BM layer boundaries -- image patches -- boundary probability maps -- dynamic programming method -- layer boundary -- DF‐LS method -- AMD dataset -- Bruch membrane -- inner limiting membrane -- deep‐forest model -- graph theory; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2018.5304
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : Age‐related macular degeneration (AMD) is a common eye disease that causes progressive degeneration of the central vision. The presence of abundant drusen is a common early feature of AMD. Optical coherence tomography (OCT) can provide detailed structure information on drusen. The physiological structure of the retinal epithelium and drusen complex (RPEDC) and the Bruch's membrane (BM) layer boundaries will be influenced by the presence of drusen with AMD. Therefore, drusen quantification is important to diagnose and cure AMD. The authors proposed an automatic method to segment the inner limiting membrane, the retinal pigment epithelium and drusen complex (RPEDC) and BM layer boundaries from OCT images with AMD (termed as deep forest for layer segmentation (DF‐LS)). In their method, image patches are extracted and used to train a deep‐forest model to predict three boundary probability maps. In addition, they modify grapy theory and dynamic programming method to find the layer boundary. Finally, the layer boundary is smoothed by using a smoothing operation. The proposed DF‐LS method is evaluated on three publicly available datasets (one healthy dataset and two AMD dataset). The proposed DF‐LS method can yield superior mean unsigned error with an average error of 0.81 pixel on Tian et al .'s dataset, and 1.35, 1.23 pixel on Chiu et al .'s and Farisu et al .'s dataset, respectively.