Many important eye diseases as well as systemic diseases manifest themselves in the retina. While a number of other anatomical structures contribute to the process of vision, this review focuses on retinal imaging and image analysis. Following a brief overview of the most prevalent causes of blindness in the industrialized world that includes age-related macular degeneration, diabetic retinopathy, and glaucoma, the review is devoted to retinal imaging and image analysis methods and their clinical implications. Methods for 2-D fundus imaging and techniques for 3-D optical coherence tomography (OCT) imaging are reviewed. Special attention is given to quantitative techniques for analysis of fundus photographs with a focus on clinically relevant assessment of retinal vasculature, identification of retinal lesions, assessment of optic nerve head (ONH) shape, building retinal atlases, and to automated methods for population screening for retinal diseases. A separate section is devoted to 3-D analysis of OCT images, describing methods for segmentation and analysis of retinal layers, retinal vasculature, and 2-D/3-D detection of symptomatic exudate-associated derangements, as well as to OCT-based analysis of ONH morphology and shape. Throughout the paper, aspects of image acquisition, image analysis, and clinical relevance are treated together considering their mutually interlinked relationships.

With the introduction of spectral-domain optical coherence tomography (OCT), much larger image datasets are routinely acquired compared to what was possible using the previous generation of time-domain OCT. Thus, the need for 3-D segmentation methods for processing such data is becoming increasingly important. We report a graph-theoretic segmentation method for the simultaneous segmentation of multiple 3-D surfaces that is guaranteed to be optimal with respect to the cost function and that is directly applicable to the segmentation of 3-D spectral OCT image data. We present two extensions to the general layered graph segmentation method: the ability to incorporate varying feasibility constraints and the ability to incorporate true regional information. Appropriate feasibility constraints and cost functions were learned from a training set of 13 spectral-domain OCT images from 13 subjects. After training, our approach was tested on a test set of 28 images from 14 subjects. An overall mean unsigned border positioning error of 5.69 ± 2.41 µm was achieved when segmenting seven surfaces (six layers) and using the average of the manual tracings of two ophthalmologists as the reference standard. This result is very comparable to the measured interobserver variability of 5.71 ± 1.98 µm.

Current techniques for segmenting macular optical coherence tomography (OCT) images have been 2-D in nature. Furthermore, commercially available OCT systems have only focused on segmenting a single layer of the retina, even though each intraretinal layer may be affected differently by disease. We report an automated approach for segmenting (anisotropic) 3-D macular OCT scans into five layers. Each macular OCT dataset consisted of six linear radial scans centered at the fovea. The six surfaces defining the five layers were identified on each 3-D composite image by transforming the segmentation task into that of finding a minimum-cost closed set in a geometric graph constructed from edge/regional information and a priori determined surface smoothness and interaction constraints. The method was applied to the macular OCT scans of 12 patients (24 3-D composite image datasets) with unilateral anterior ischemic optic neuropathy (AION). Using the average of three experts’ tracings as a reference standard resulted in an overall mean unsigned border positioning error of 6.1 ± 2.9 µm, a result comparable to the interobserver variability (6.9 ± 3.3 µm). Our quantitative analysis of the automated segmentation results from AION subject data revealed that the inner retinal layer thickness for the affected eye was 24.1 µm (21%) smaller on average than for the unaffected eye (P < 0.001), supporting the need for segmenting the layers separately.

Purpose To determine whether type 1 diabetes preferentially affects the inner retinal layers by comparing the thickness of six retinal layers in type 1 diabetic patients who have no or minimal diabetic retinopathy (DR) with those of age- and sex-matched healthy controls. Methods Fifty-seven patients with type 1 diabetes with no (n = 32) or minimal (n = 25) DR underwent full ophthalmic examination, stereoscopic fundus photography, and optical coherence tomography (OCT). After automated segmentation of intraretinal layers of the OCT images, mean thickness was calculated for six layers of the retina in the fovea, the pericentral area, and the peripheral area of the central macula and were compared with those of an age- and sex-matched control group. Results In patients with minimal DR, the mean ganglion cell/ inner plexiform layer was 2.7 μm thinner (95% confidence interval [CI], 2.1– 4.3 μm) and the mean inner nuclear layer was 1.1 μm thinner (95% CI, 0.1–2.1 μm) in the pericentral area of the central macula compared to those of age-matched controls. In the peripheral area, the mean ganglion cell/inner plexiform layer remained significantly thinner. No other layers showed a significant difference. Conclusions Thinning of the total retina in type 1 diabetic patients with minimal retinopathy compared with healthy controls is attributed to a selective thinning of inner retinal layers and supports the concept that early DR includes a neurode-generative component.