OCT segmentation algorithms have recently instituted segmentation of the retina GCL-IPL layer complex within the macula. The ganglion cell layer contains the cell bodies that give rise to the axons that make up the RNFL and optic nerve. Thinning of the GCL-IPL layer in the macula has been found to correlate with visual loss and corresponding loss of visual field sensitivity from optic nerve diseases such as glaucoma, optic neuritis, ischemic optic neuropathy, hereditary optic neuropathy, toxic optic neuropathy, optic nerve glioma, and idiopathic intracranial hypertension (4–12).
Perhaps the most intriguing use of the macular GCL-IPL thickness is in the evaluation of neuronal loss in the presence of optic disc edema. Disc edema and associated increase in RNFL thickness prevent accurate assessment of concomitant axonal loss by OCT due to the significant swollen axons of the optic nerve. This is commonly seen in papilledema or optic disc edema associated with acute and subacute ischemic optic neuropathy and optic neuritis. The problem can be theoretically overcome by analyzing the inner layers of the macula, specifically the GCL-IPL complex (60). Unfortunately, the commercially available segmentation algorithms are prone to segmentation failures of the GCL-IPL complex, especially when there is low signal strength, optic nerve edema, or structural abnormalities in the outer retinal layers (which affects segmentation of the inner retinal layers) (61–63). One sign of inaccurate inner layer segmentation is the appearance of nonpathologic shapes in the thickness and probability maps, such as a corner of abnormal thinning (Figs. 2, 3). Errors in GCL-IPL segmentation can also often appear as segments of blue (thinning) on the thickness map in the shape of spokes on a wheel (“propeller sign” in Fig. 13). A GCL-IPL reading of less than 40 μm is also typically indicative of areas of segmentation error. On the B-scan, the algorithm's identification of the boundaries of the ganglion cell layer and inner plexiform layer often collapse together in the areas of artifact, producing an artifactual thinning (Figs. 2, 3). More robust 3D segmentation algorithms available in research are able to overcome these segmentation errors (Fig. 2) (12).
Within the macula, the most variation in thickness among normal eyes is in the perifoveal location due to a wide variation in thickness profile of the inner retina immediately surrounding the fovea. Therefore, an abnormal probability map in the perifoveal location should be scrutinized carefully and correlated with the clinical exam and functional tests. It is important to ensure that the fovea was correctly identified and centered by the OCT analysis; otherwise, artifactual thickening and thinning of the retina will be displayed as abnormal. However, true atrophy of the GCL-IPL may also cause perifoveal thinning and enlargement of the foveal depression, making it difficult to differentiate focal pathological thinning in the perifoveal inner retina compared to normal variation in thickness in this location.
The repeatability of scans is dependent on a number of factors. It is important to scan the same retinal location with each subsequent examination. Comparing slightly different retinal areas of a scan can lead to an increase in variability, especially in pathologic eyes such as those with macular or optic nerve edema. Many OCT machines have the capability of registration, which will match each subsequent OCT image to a reference baseline scan using landmarks such as the position of retinal vessels and the location of the optic disc. This has been demonstrated to decrease the variability between subsequent scans (2). In addition, newer OCT machines have implemented a gaze tracker, which improves repeatability of subsequent scans. Faster scanning, as will become available with swept source OCT, will also improve reproducibility since retina movements occurring during scans acquired at a faster rate will have less influence.
It is important to note that measurements from different OCT machines cannot be compared directly for a given patient (2,64–68). Different OCT machines provide different thickness measurements because of the differences in the way the scans are obtained, which includes scan speed and number and density of scans obtained. In addition, the commercially available OCT machines each have their own different, proprietary algorithm that may segment the layers differently. For example, Stratus OCT measures the total macular thickness with boundaries between the internal limiting membrane and the hyper-reflective band corresponding to the interface between the inner and the outer segment of photoreceptors while Spectralis OCT uses the boundaries of the internal limiting membrane and the retinal pigment epithelium to measure the total macular thickness of the retina (64). The differences in the algorithm's defined boundaries of the retina are exaggerated in pathologic eyes, such as choroidal neovascularization from macular degeneration (64).
In addition to using different reference boundaries to define thickness, the specific algorithm used for segmentation can influence repeatability measurements. Sohn et al (69) analyzed the intervisit variability of Spectralis OCT obtained macular thickness measurements from patients with diabetic macular edema and found that an independent 3-dimensional graph segmentation algorithm, the Iowa Reference Algorithm, had slightly better repeatability compared to the manufacturer Spectralis algorithm when analyzing the same Spectralis spectral domain OCT images.
Much like the systematic approach recommended for interpreting automated Humphrey visual fields, we propose a similar approach to interpreting OCT scans.
OCT has revolutionized the ability to detect subtle optic nerve disease. However, misinterpretation of artifacts on OCT can lead to errors in clinical judgment, where both false reassurance and false concern can be created. Using a systematic approach to OCT analysis allows one to identify and interpret these artifacts. It is important to note that OCT technology is constantly evolving. Much like spectral domain OCT has largely supplanted time-domain OCT, a new method of faster and more detailed imaging, such as swept source imaging, will likely replace spectral domain OCT in the future. New technology will bring new insight into disease processes, but will likely also bring new artifacts that we will need to be prepared to identify and interpret correctly. Regardless of the future changes in OCT imaging, keeping a systematic approach to OCT analysis will allow us to avoid or minimize the clinical misinterpretations of artifacts on OCT.
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