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Fei Shi, Xinjian Chen, Weifang Zhu, Dehui Xiang, Enting Gao, Haoyu Chen; Automated 3-D Retinal Layer Segmentation of Macular OCT Images with Retinal Pigment Epithelial Detachments. Invest. Ophthalmol. Vis. Sci. 2014;55(13):4798.
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© ARVO (1962-2015); The Authors (2016-present)
Automated retinal layer segmentation of OCT images has been successful for normal eyes but remains an open problem for eyes with retinal diseases. We present a method to automatically segment the retinal layers in 3-D OCT data with retinal pigment epithelial detachments (PEDs), which is a prominent feature of many chorioretinal disease processes.
Macular-centered SD-OCT (Topcon, 512×64×480 voxels, 11.72×93.75×3.50µm3) of 9 eyes diagnosed with PEDs were acquired. Automated 3-D retinal layer segmentation based on graph search was achieved as follows: First, surfaces 1-6, which were not dramatically affected by PED, and a surface combined by surface 7 and 10 (surface 7’) were detected by a multi-resolution approach. Then the floor of RPE layer (surface 11) was detected below surface 7’ with large smoothness constraints to allow the abrupt change caused by PED. A surface (surface 12) indicating the normal RPE floor was detected using the same cost function but small smoothness constraints. Subsequently, the PED footprints indicating which A-scans were associated with PED were obtained by calculating the distance between surface 11 and 12. Finally, the OCT volume was flattened using a reference surface combined by surface 11 and 12, which removed the PED area and regained the normal appearance of surfaces 7 and below. In this flattened image, surface 7-10 were detected with corrections around the PED area.
Manual segmentation was performed by two experts for surfaces 1,2,4-7,10 and 11, which were discernible to human eyes. For each OCT volume 10 B-scans (every sixth among the total 64) were selected for manual tracing, and the average z-position of the two results were used as the reference standards. The overall mean unsigned border error of the automatic segmentation was 2.25±0.69 voxels (7.89±2.41µm) and was comparable to the inter-observer variability 2.15±0.53 voxels (7.54±1.87µm).
Automated 3-D retinal layer segmentation of macular OCT images with PED has been achieved. Further analysis on retinal layer thickness, intensity and PED area/volume can be applied based on the segmentation result, which may help the diagnosis of various diseases related to PED.
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