Commercial optical coherence tomography (OCT) systems use global signal quality indices to quantify scan quality. Signal quality can vary throughout a scan, contributing to local retinal nerve fibre layer segmentation errors (SegE). The purpose of this study was to develop an automated method, using local scan quality, to predict SegE.
Good-quality (global signal strength (SS)≥6; manufacturer specification) peripapillary circular OCT scans (fast retinal nerve fibre layer scan protocol; Stratus OCT; Carl Zeiss Meditec, Dublin, California, USA) were obtained from 6 healthy, 19 glaucoma-suspect and 43 glaucoma subjects. Scans were grouped based on SegE. Quality index (QI) values were computed for each A-scan using software of our own design. Logistic mixed-effects regression modelling was applied to evaluate SS, global mean and SD of QI, and the probability of SegE.
The difference between local mean QI in SegE regions and No-SegE regions was −5.06 (95% CI −6.38 to 3.734) (p<0.001). Using global mean QI, QI SD and their interaction term resulted in the model of best fit (Akaike information criterion=191.8) for predicting SegE. Global mean QI≥20 or SS≥8 shows little chance for SegE. Once mean QI<20 or SS<8, the probability of SegE increases as QI SD increases.
When combined with a signal quality parameter, the variation of signal quality between A-scans provides significant information about the quality of an OCT scan and can be used as a predictor of segmentation error.