Purpose 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.

Methods 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.

Results 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.

Conclusions 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.