Purpose: To identify the retinal layer predominantly affected in eyes with subclinical and clinical macular edema in diabetes type 2. Methods: A cohort of 194 type 2 diabetic eyes/patients with mild nonproliferative diabetic retinopathy (ETDRS levels 20/35) were examined with Cirrus spectral-domain optical coherence tomography (OCT) at the baseline visit (ClinicalTrials.gov identifier: NCT01145599). Automated segmentation of the retinal layers of the eyes with subclinical and clinical macular edema was compared with a sample of 31 eyes from diabetic patients with normal OCT and an age-matched control group of 58 healthy eyes. Results: From the 194 eyes in the study, 62 had subclinical macular edema and 12 had clinical macular edema. The highest increases in retinal thickness (RT) were found in the inner nuclear layer (INL; 33.6% in subclinical macular edema and 81.8% in clinical macular edema). Increases were also found in the neighboring layers. Thinning of the retina was registered in the retinal nerve fiber, ganglion cells and inner plexiform layers in the diabetic eyes without macular edema. Conclusions: The increase in RT occurring in diabetic eyes with macular edema is predominantly located in the INL but extends to neighboring retinal layers indicating that it may be due to extracellular fluid accumulation.

WeVaR produced intra- and intervisit fundus mosaics with higher registration accuracy than Merge Eye Care PACS and i2k Retina. Merge Eye Care PACS had higher registration failures than the other two programs. Highly accurate registration methods, such as WeVaR, may potentially be used for more efficient human grading and in computer-aided screening systems for detecting DR progression.

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Changes in MA activity measured with RetmarkerDR and in central retinal thickness in eyes with mild nonproliferative diabetic retinopathy and diabetes type 2 are able to identify eyes at risk of progression. These eyes/patients should be selected for inclusion in future clinical trials of drugs targeted to prevent diabetic retinopathy progression to vision-threatening complications. (ClinicalTrials.gov number, NCT01145599.)

Abstract-People with diabetes mellitus need annual screening to check for the development of diabetic retinopathy (DR). Tracking small retinal changes due to early diabetic retinopathy lesions in longitudinal fundus image sets is challenging due to intra-and inter-visit variability in illumination and image quality, the required high registration accuracy, and the subtle appearance of retinal lesions compared to other retinal features. This paper presents a robust and flexible approach for automated detection of longitudinal retinal changes due to small red lesions by exploiting normalized fundus images that significantly reduce illumination variations and improve the contrast of small retinal features. To detect spatio-temporal retinal changes, the absolute difference between the extremes of the multiscale blobness responses of fundus images from two time-points is proposed as a simple and effective blobness measure. DR related changes are then identified based on several intensity and shape features by a support vector machine classifier. The proposed approach was evaluated in the context of a regular diabetic retinopathy screening program involving subjects ranging from healthy (no retinal lesion) to moderate (with clinically relevant retinal lesions) DR levels. Evaluation shows that the system is able to detect retinal changes due to small red lesions with a sensitivity of 80% at an average false positive rate of 1 and 2.5 lesions per eye on small and large fields-of-view of the retina, respectively.