Evaluation of Automated Segmentation Algorithms for Optic Nerve Head Structures in Optical Coherence Tomography Images.

PURPOSE

To compare the identification of optic nerve head (ONH) structures in optical coherence tomography images by observers and automated algorithms.

METHODS

ONH images in 24 radial scan sets by optical coherence tomography were obtained in 51 eyes of 29 glaucoma patients and suspects. Masked intraobserver and interobserver comparisons were made of marked endpoints of Bruch's membrane opening (BMO) and the anterior lamina cribrosa (LC). BMO and LC positional markings were compared between observer and automated algorithm. Repeated analysis on 20 eyes by the algorithm was compared. Regional ONH data were derived from the algorithms.

RESULTS

Intraobserver difference in BMO width was not significantly different from zero (P ≥ 0.32) and the difference in LC position was less than 1% different (P = 0.04). Interobserver were slightly larger than intraobserver differences, but interobserver BMO width difference was 0.36% (P = 0.63). Mean interobserver difference in LC position was 14.74 μm (P = 0.004), 3% of the typical anterior lamina depth (ALD). Between observer and algorithm, BMO width differed by 1.85% (P = 0.23) and mean LC position was not significantly different (3.77 μm, P = 0.77). Repeat algorithmic analysis had a mean difference in BMO area of 0.38% (P = 0.47) and mean ALD difference of 0.54 ± 0.72%. Regional ALD had greater variability in the horizontal ONH regions. Some individual outlier images were not validly marked by either observers or algorithm.

CONCLUSIONS

Automated identification of ONH structures is comparable to observer markings for BMO and anterior LC position, making BMO a practical reference plane for algorithmic analysis.