Abstract
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.
Highlights
Automated OCT images analysis allows detection and quantitative assessment of retinal abnormalities [1, 2]
The first dataset is a local dataset from the UMN ophthalmology clinic containing 600 OCT scans collected from 24 exudative age related macular degeneration (EAMD) subjects which were taken using the Heidelberg Spectralis imaging system
In line with advanced EAMD treatment methods, OCT has emerged as an essential adjunct for the diagnosis and monitoring of this disease and the ability to accurately segment fluid as an EAMD biomarker is a prerequisite for ophthalmologists in treatment process
Summary
Automated OCT images analysis allows detection and quantitative assessment of retinal abnormalities [1, 2]. The analysis of fluid and other abnormalities is a challenging task and has been of greater interest in recent years [3, 4]. In this work, automated analysis of exudative age related macular degeneration (EAMD) is carried out, which is characterized by the growth of abnormal blood vessels from the choroidal vasculature, and the resultant fluid leakage into the intra-retinal, sub-retinal, and sub-retinal pigment epithelium (RPE) spaces. The standard treatment for this condition is guided by the presence and quantity of this fluid [5, 6].
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