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Neil O'Leary, David P. Crabb, David F. Garway-Heath; An In Silico Model of Scanning Laser Tomography Image Series: An Alternative Benchmark for the Specificity of Progression Algorithms. Invest. Ophthalmol. Vis. Sci. 2010;51(12):6472-6482. doi: 10.1167/iovs.10-5355.
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© 2017 Association for Research in Vision and Ophthalmology.
There is no gold-standard measurement of glaucomatous structural progression against which to validate software progression algorithms. A computer model was developed and validated to simulate stable series of Heidelberg Retina Tomograph II (HRT; Heidelberg Engineering, Heidelberg, Germany) images, with realistic topographic variability, suitable for benchmarking false-positive rates of progression algorithms.
Three confocal image stacks were selected from each of five sets of HRT II scans, obtained within 6 weeks in 127 eyes of 66 patients. For each eye, a simulated series was propagated from one baseline confocal stack by adding fixational eye movements, photon-counting, and electronic measurement noise. Simulated confocal stacks were imported into the HRT software to generate topography images. Real and simulated image comparisons were quantified with the mean pixel height standard deviation (MPHSD), image cross-correlation (CC) of pixel-wise variability maps, and the rim area (RA) coefficient of variation (CV).
The mean difference (95% limits of agreement; LoA) in MPHSD between real and simulated images was 3.5 μm (−20.9 to 28.8 μm) within mean topographies and 2.0 μm (−5.4 to 9.3 μm) between mean topographies. The mean CC between real and simulated spatial variability maps was 0.58 within mean topographies and 0.54 between mean topographies. The mean difference (95% LoA) between real and simulated mean topography RA CV was −2.1% (−17.6% to +13.4%). Variability about anatomic features was well reproduced.
Simulation realistically reproduces variability in real, stable images acquired over a short period. Stability in clinical datasets is uncertain, whereas in these modeled series, it is certain. This method provides benchmark datasets on which the specificity of progression algorithms can be tested.
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