Fully-automated segmentation of fluid regions in exudative age-related macular degeneration subjects: Kernel graph cut in neutrosophic domain

• Published in: PloS one Vol. 12; no. 10; p. e0186949
• Main Authors: Rashno, Abdolreza, Nazari, Behzad, Koozekanani, Dara D, Drayna, Paul M, Sadri, Saeed, Rabbani, Hossein, Parhi, Keshab K
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 23-10-2017; Public Library of Science (PLoS)
• Subjects: Age; Age related diseases; Algorithms; Analysis; Artificial intelligence; Automation; Biology and Life Sciences; Care and treatment; Computer and Information Sciences; Computer engineering; Cysts; Diagnosis; Epithelium; Exudation; Fluids; Humans; Image Interpretation, Computer-Assisted; Macular degeneration; Medicine and Health Sciences; Methods; Neural networks; Neurosciences; Optics; Pattern recognition; Physical Sciences; Physiological aspects; Regression analysis; Research and Analysis Methods; Retina; Retinal pigment epithelium; Segmentation; Sensitivity; Shortest-path problems; Theory; Tomography; Tomography, Optical Coherence; Wet Macular Degeneration - diagnostic imaging; Iran; United States
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0186949

A fully-automated method based on graph shortest path, graph cut and neutrosophic (NS) sets is presented for fluid segmentation in OCT volumes for exudative age related macular degeneration (EAMD) subjects. The proposed method includes three main steps: 1) The inner limiting membrane (ILM) and the retinal pigment epithelium (RPE) layers are segmented using proposed methods based on graph shortest path in NS domain. A flattened RPE boundary is calculated such that all three types of fluid regions, intra-retinal, sub-retinal and sub-RPE, are located above it. 2) Seed points for fluid (object) and tissue (background) are initialized for graph cut by the proposed automated method. 3) A new cost function is proposed in kernel space, and is minimized with max-flow/min-cut algorithms, leading to a binary segmentation. Important properties of the proposed steps are proven and quantitative performance of each step is analyzed separately. The proposed method is evaluated using a publicly available dataset referred as Optima and a local dataset from the UMN clinic. For fluid segmentation in 2D individual slices, the proposed method outperforms the previously proposed methods by 18%, 21% with respect to the dice coefficient and sensitivity, respectively, on the Optima dataset, and by 16%, 11% and 12% with respect to the dice coefficient, sensitivity and precision, respectively, on the local UMN dataset. Finally, for 3D fluid volume segmentation, the proposed method achieves true positive rate (TPR) and false positive rate (FPR) of 90% and 0.74%, respectively, with a correlation of 95% between automated and expert manual segmentations using linear regression analysis.