Purchase this article with an account.
Robert W. Knighton, Giovanni Gregori, Donald L. Budenz; Variance Reduction in a Dataset of Normal Macular Ganglion Cell Plus Inner Plexiform Layer Thickness Maps with Application to Glaucoma Diagnosis. Invest. Ophthalmol. Vis. Sci. 2012;53(7):3653-3661. doi: 10.1167/iovs.12-9719.
Download citation file:
© ARVO (1962-2015); The Authors (2016-present)
To examine the similarities and differences in the shape of the macular ganglion cell plus inner plexiform layers (GCL+IPL) in a healthy human population, and seek methods to reduce population variance and improve discriminating power.
Macular images of the right eyes of 23 healthy subjects were obtained with spectral domain optical coherence tomography. The thickness of GCL+IPL was determined by manual segmentation, areas with blood vessels were removed, and the resulting maps were fit by smooth surfaces in polar coordinates centered on the fovea.
The mean GCL+IPL thickness formed a horizontal elliptical annulus. The variance increased toward the center and was highest near the foveal edge. Individual maps differed in foveal size and overall GCL+IPL thickness. Foveal size correction by radially shifting individual maps to the same foveal size as the mean map reduced perifoveal variance. Thickness alignment by shifting individual maps axially, then radially, to match the mean map reduced overall variance. These transformations had very little effect on the population mean.
Simple transformations of individual GCL+IPL thickness maps to a canonical form can considerably reduce the population variance in a sample of normal eyes, likely improving the ability to discriminate abnormal maps. The transformations considered here preserve the local geometry of the thickness maps. When used on a patient's map, they can produce a deviation map that provides a meaningful measurement of the size of local thickness deviations and allows estimation of the number of ganglion cells lost in a glaucomatous defect.
This PDF is available to Subscribers Only