Automated surface segmentation of internal limiting membrane in spectral-domain optical coherence tomography volumes with a deep cup using a 3-D range expansion approach

Spectral-domain optical coherence tomography (SD-OCT) is used clinically for the diagnosis and management of glaucoma, with the internal limiting membrane (ILM) being one important structure of interest as it reflects the upper bounding surface of the optic cup. In cases of severe glaucoma, the boundary of the optic cup can become very steep and difficult to segment. Because of this difficulty, current automated surface segmentation algorithms will frequently fail in cases of deep cupping and provide a cup that is too shallow. In this work, we propose a 3-D graph-theoretic segmentation method based on the range expansion algorithm for accurate ILM segmentation in SD-OCT volumes of subjects with severe glaucoma. The performance of this approach was validated on 10 optic nerve head (ONH) centered OCT volumes obtained from 10 glaucomatous subjects. We computed the unsigned mean surface positioning (UMSP) error and unsigned average symmetric surface distance (UASSD) error with respect to manual tracings (ground truth) from one independent expert observer. Comparing to the widely used graph search method, our proposed method achieved a significant improvement in the UMSP error which was reduced from 7.69±3.23 μm to 6.35±1.35 μm (p <; 0.04), and in the UASSD error which was reduced from 3.86±0.54 μm to 3.40±0.17μm (p <; 0.07). It has high potential for future extension to multiple surface segmentation and clinical applicability.