Abstract
We propose a novel automated volumetric segmentation method to detect and quantify retinal fluid on optical coherence tomography (OCT). The fuzzy level set method was introduced for identifying the boundaries of fluid filled regions on B-scans (x and y-axes) and C-scans (z-axis). The boundaries identified from three types of scans were combined to generate a comprehensive volumetric segmentation of retinal fluid. Then, artefactual fluid regions were removed using morphological characteristics and by identifying vascular shadowing with OCT angiography obtained from the same scan. The accuracy of retinal fluid detection and quantification was evaluated on 10 eyes with diabetic macular edema. Automated segmentation had good agreement with manual segmentation qualitatively and quantitatively. The fluid map can be integrated with OCT angiogram for intuitive clinical evaluation.
Highlights
Diabetic retinopathy (DR) is a microvascular disease characterized by hyperpermeability, capillary occlusion, and neovascularization [1]
We present and validate an automated volumetric segmentation algorithm based on fuzzy level-set method [19] to identify and quantify retinal fluid, including intraretinal fluid (IRF) and subretinal fluid (SRF), on Optical coherence tomography (OCT) structural images
We developed an automated volumetric segmentation method to quantify retinal fluid (IRF and SRF) on OCT which involves three main steps: (1) segment and flatten retinal layers; (2) identify retinal fluid space using a fully automated and self-adaptive model on OCT cross-sections from three orthogonal directions; (3) remove remaining artifacts by identifying morphological characteristics and vascular shadowing
Summary
Diabetic retinopathy (DR) is a microvascular disease characterized by hyperpermeability, capillary occlusion, and neovascularization [1]. A few state-of-the-art algorithms [11,12,13,14,15,16,17,18] provided fluid segmentation methods on clinical two dimensional (2D) OCT images. These have been used to classify the fluid associated abnormalities based on extraordinary retinal layer texture and structure gradient in DME. We present and validate an automated volumetric segmentation algorithm based on fuzzy level-set method [19] to identify and quantify retinal fluid, including intraretinal fluid (IRF) and subretinal fluid (SRF), on OCT structural images. By registering the structural (fluid accumulation) and functional (blood flow) information into a single 3D volume, clinicians can intuitively evaluate pathological structures and microvascular dysfunction simultaneously
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
