Abstract
We present a fully automatic algorithm to identify fluid-filled regions and seven retinal layers on spectral domain optical coherence tomography images of eyes with diabetic macular edema (DME). To achieve this, we developed a kernel regression (KR)-based classification method to estimate fluid and retinal layer positions. We then used these classification estimates as a guide to more accurately segment the retinal layer boundaries using our previously described graph theory and dynamic programming (GTDP) framework. We validated our algorithm on 110 B-scans from ten patients with severe DME pathology, showing an overall mean Dice coefficient of 0.78 when comparing our KR + GTDP algorithm to an expert grader. This is comparable to the inter-observer Dice coefficient of 0.79. The entire data set is available online, including our automatic and manual segmentation results. To the best of our knowledge, this is the first validated, fully-automated, seven-layer and fluid segmentation method which has been applied to real-world images containing severe DME.
Highlights
Diabetic retinopathy is the leading cause of blindness among working-aged adults in the United States [1]
As a step towards this goal, we present a fully automated algorithm based on kernel regression (KR) [53] and our previously described graph theory and dynamic programming (GTDP) framework [46, 54, 55] to identify fluid-filled regions and seven retinal layers on spectral domain (SD)-optical coherence tomography (OCT) images of eyes with diabetic macular edema (DME)
To realize the improvement of our new KR + GTDP segmentation algorithm compared to existing methods that do not consider the presence of fluid, we evaluated the performance of our GTDP algorithm developed for normal eyes on the DME data set
Summary
Diabetic retinopathy is the leading cause of blindness among working-aged adults in the United States [1]. Among those affected, approximately 21 million people develop diabetic macular edema (DME) [2]. Retinal hypoxia results in increased production of vascular endothelial growth factors (VEGF) and other signaling cascades [3,4,5]. These further progress DME through mechanisms such as cytotoxic damage to retinal fluid transport cells [6,7,8,9]. The imbalance between vascular leakage and fluid transport leads to retinal edema and vision loss
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