Artifacts in automatic retinal segmentation using different optical coherence tomography instruments.

TL;DR: Spectral-domain OCT instruments reduce, but do not eliminate, errors in retinal segmentation, and accurate segmentation is lower in pathologic eyes compared with normal eyes for all instruments.

Abstract: PURPOSE The purpose of this study was to compare and evaluate artifact errors in automatic inner and outer retinal boundary detection produced by different time-domain and spectral-domain optical coherence tomography (OCT) instruments. METHODS Normal and pathologic eyes were imaged by six different OCT devices. For each instrument, standard analysis protocols were used for macular thickness evaluation. Error frequencies, defined as the percentage of examinations affected by at least one error in retinal segmentation (EF-exam) and the percentage of total errors per total B-scans, were assessed for each instrument. In addition, inner versus outer retinal boundary delimitation and central (1,000 microm) versus noncentral location of errors were studied. RESULTS The study population of the EF-exam for all instruments was 25.8%. The EF-exam of normal eyes was 6.9%, whereas in all pathologic eyes, it was 32.7% (P < 0.0001). The EF-exam was highest in eyes with macular holes, 83.3%, followed by epiretinal membrane with cystoid macular edema, 66.6%, and neovascular age-related macular degeneration, 50.3%. The different OCT instruments produced different EF-exam values (P < 0.0001). The Zeiss Stratus produced the highest percentage of total errors per total B-scans compared with the other OCT systems, and this was statistically significant for all devices (P < or = 0.005) except the Optovue RTvue-100 (P = 0.165). CONCLUSION Spectral-domain OCT instruments reduce, but do not eliminate, errors in retinal segmentation. Moreover, accurate segmentation is lower in pathologic eyes compared with normal eyes for all instruments. The important differences in EF among the instruments studied are probably attributable to analysis algorithms used to set retinal inner and outer boundaries. Manual adjustments of retinal segmentations could reduce errors, but it will be important to evaluate interoperator variability.