Macular ganglion cell-inner plexiform layer: automated detection and thickness reproducibility with spectral domain-optical coherence tomography in glaucoma

Abstract

Purpose: To demonstrate the capability of SD-OCT to measure macular retinal ganglion cell-inner plexiform layer (GCIPL) thickness and to assess its reproducibility in glaucomatous eyes.

Methods: Fifty-one glaucomatous eyes (26 mild, 11 moderate, 14 severe) of 51 patients underwent macular scanning using the Cirrus HD-OCT (Carl Zeiss Meditec, Dublin, CA) macula 200×200 acquisition protocol. Five scans were obtained on 5 days within 2 months. The ganglion cell analysis (GCA) algorithm was used to detect the macular GCIPL and to measure the thickness of the overall average, minimum, superotemporal, superior, superonasal, inferonasal, inferior, and inferotemporal GCIPL. The reproducibility of the measurements was evaluated with intraclass correlation coefficients (ICCs), coefficients of variation (COVs), and test-retest standard deviations (TRTSDs).

Results: Segmentation and measurement of GCIPL thickness were successful in 50 of 51 subjects. All ICCs ranged between 0.94 and 0.98, but ICCs for average and superior GCIPL parameters (0.97-0.98) were slightly higher than for inferior GCIPL parameters (0.94-0.97). All COVs were <5%, with 1.8% for average GCIPL and COVs for superior GCIPL parameters (2.2%-3.0%) slightly lower than those for inferior GCIPL parameters (2.5%-3.6%). The TRTSD was lowest for average GCIPL (1.16 μm) and varied from 1.43 to 2.15 μm for sectoral GCIPL CONCLUSIONS: The Cirrus HD-OCT GCA algorithm can successfully segment macular GCIPL and measure GCIPL thickness with excellent intervisit reproducibility. Longitudinal monitoring of GCIPL thickness may be possible with Cirrus HD-OCT for assessing glaucoma progression.

Figures

Figure 1.

Cirrus OCT en face image (A) displaying the 6 × 6 mm portion of the retina scanned by the acquisition protocol with the annulus (area between the two white rings) within the cube used by the Cirrus GCA algorithm to measure the thickness of the GCIPL. (B, C) Segmentation of macular intraretinal layers from a horizontal and a vertical tomogram, respectively, with the Cirrus GCA algorithm. Boundaries (top to bottom): red, internal limiting membrane; green, RNFL-RGC boundary; light blue, IPL-INL boundary; magenta, IPL-OPL boundary; dark blue, Bruch's membrane. Layers (top to bottom): RNFL, GCIPL, internal nuclear layer (INL), and OPL/photoreceptors.

Figure 2.

GCIPL thickness maps (the denser the orange/yellow ring, the thicker the GCIPL) of a normal eye (A) and an eye with severe glaucoma (B). GCIPL deviation map (C) and significance map (D) of the same eye shown in B. The red on the deviation map indicates regions with GCIPL thickness outside normal limits. The significance map shows (clockwise) thicknesses of the superior, superonasal, inferonasal, inferior, inferotemporal, and superotemporal sectors of the annulus and the average and minimum GCIPL (box).

Figure 3.

Intervisit reproducibility of GCIPL thickness measured from five different visits within a 2-month period for an eye with mild (A), moderate (B), and severe (C) glaucoma. Note the consistency in average GCIPL thickness over the five sessions in all three eyes.

Figure 4.

Scatter plot of the relationship between the test-retest standard deviations versus the mean of average GCIPL thickness for each of the 50 eyes in the study.

Figure 5.

Linear regression plot of the relationship between VF mean deviation and average GCIPL thickness (A), minimum GCIPL thickness (B), and GCIPL thickness of the superotemporal (C) and inferotemporal (D) sectors of the macular.