Technique for Detecting Serial Topographic Changes in the Optic Disc and Peripapillary Retina Using Scanning Laser Tomography

To describe and evaluate a new statistical technique for detecting topographic changes in the optic disc and peripapillary retina measured with confocal scanning laser tomography. The 256x256-pixel array of topographic height values obtained with each image from the Heidelberg Retina Tomograph (Heidelberg Engineering, Heidelberg, Germany) was divided into an array of 64x64 superpixels, where each superpixel contained 16 (i.e., 4x4) pixels. An analysis of variance technique was developed to analyze each superpixel with three baseline and three follow-up images. The performance of the technique was tested with and without adjustment for spatial correlation of topographic values using computer simulations and with real data from a normal control subject and a patient with progressive glaucomatous disc change. Computer simulation with fixed population means and variance, and varying spatial correlation showed a monotonically increasing number of superpixels with significant test results (false positives), with 20% false-positives when the spatial correlation was 0.8 (the approximate median value in real patient data). The number of false-positive results was similar (17%) in serial images of a normal subject. When corrected for spatial correlation, the number of false-positives was independent of the level of spatial correlation and remained at the expected value of less than 5% in both simulations and real data. Although the number of significant test results in the patient with progressive glaucoma decreased after correction for spatial correlation, the change was readily apparent. Statistical power to detect mean differences in topographic values ranging from 0.5 to 4.0 SDs in computer simulation showed low power for changes of 1 SD or less, but increased dramatically with larger changes. This technique has a high level of sensitivity to detect changes in the optic disc while maintaining a high level of specificity.