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Abstract #56205 Published in IGR 16-1

Automated segmentation of optic nerve head structures with optical coherence tomography

Almobarak FA; O'Leary N; Reis AS; Sharpe GP; Hutchison DM; Nicolela MT; Chauhan BC
Investigative Ophthalmology and Visual Science 2014; 55: 1161-1168

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PURPOSE: To quantify and characterize the difference between automated and manual segmentation of optic nerve head structures with spectral-domain optical coherence tomography (SD-OCT). METHODS: Optic nerve head radial scans in 107 glaucoma patients and 48 healthy controls were conducted with SD-OCT. Independent segmentations of the internal limiting membrane (ILM) and Bruch's membrane opening (BMO) were performed manually with custom software and with an automated algorithm in each radial scan. The minimum distance between BMO and ILM, termed BMO-minimum rim width (BMO-MRW) was calculated with each segmentation method. Absolute differences between automated and manual segmentations of ILM (ΔILM) and BMO (ΔBMO), and the resulting computation of BMO-MRW (ΔBMO-MRW) were computed. Finally, the relationship between image quality score and ΔILM and ΔBMO was explored. RESULTS: The median (interquartile range, IQR) ΔILM was 8.9 (6.5, 13.4) μm in patients and 7.3 (5.3, 9.9) μm in controls. The corresponding values for ΔBMO were 11.5 (6.6, 22.1) μm and 12.4 (6.8, 25.4) μm. Subject-averaged ΔILM was higher in patients than controls (P < 0.01); however, mean ΔBMO was not (P = 0.09). The median (IQR) subject-averaged absolute ΔBMO-MRW was 13.4 (10.6, 16.8) μm in patients and 12.1 (10.0, 16.8) μm in controls and not statistically different (P = 0.21). Mean image quality score was statistically higher in controls than patients (P = 0.03) but not related to subject-averaged ΔILM or ΔBMO. CONCLUSIONS: In individual scans, the median difference in ILM and BMO segmentations was <2 and <3 image pixels, respectively. There were no differences between patients and controls in ΔBMO-MRW.

Full article

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Classification:

6.9.2.2 Posterior (Part of: 6 Clinical examination methods > 6.9 Computerized image analysis > 6.9.2 Optical coherence tomography)

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