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Sohini RoyChowdhury, Keshab Parhi, Michael Reinsbach, Dara Koozekanani; Automated Intra-Retinal Layer Thickness Profile Analysis in Optical Coherence Tomography Images by Iterative High-Pass Filtering. Invest. Ophthalmol. Vis. Sci. 2014;55(13):4797.
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© ARVO (1962-2015); The Authors (2016-present)
To develop an automated system that segments seven retinal sub-layers in OCT images of both normal eyes and eyes with diabetic macular edema (DME).
Heidelberg Spectralis OCT images were used. A total of 87 SD-OCT macular line scans were used with 22 of the scans containing significant disruptions of the retinal micro-structure due to cysts. For the algorithm each OCT image was subjected to automated speckle noise removal using both a Fourier-domain based error metric and a Wiener deconvolution algorithm. Next, an iterative multi-resolution filtering algorithm was used to detect the next most significant layer using high-pass filtering. Finally, the automated thickness profiles for each intraretinal layer were compared with manually marked ground-truth.
We observe that for the images with no retinal disorganization, the mean and standard deviation of error in μm for segmenting each retinal layer is as follows: NFL (10.23±2.27), IPL (22.68±1.74), INL (16.67±4.3), ONL (6.61±4.03), PIS (2.29±0.61), PE (7.29±3.17). For images with significant retinal disorganization, these errors are as follows: NFL (6.07±2.53), IPL (32.68±4.23), INL (20.59±3.17), ONL (13.68±4.13), PIS (3.729±1.91), PE (11.07±2.8). The mean measured intra-retinal thickness profiles in μm are as follows: Outer layers (106.09± 8.25), Inner layers (86.50 ±4.89), INL (38.11±5.15), ONL (65.42±4.94), Inner/Outer segments (38.95±2.92). Finally, the mean retinal layer thickness using the automated algorithm is compared to the manual segmentation results using the correlation coefficient (r) and the R2 metric as follows: Outer layers (r=0.86, R2 =0.74), Inner layers (r=0.74, R2 =0.54), INL (r=0.90, R2 =0.81), ONL (r=0.97, R2 =0.94), Inner/Outer segments (r=0.91, R2 =0.82). The complete segmentation algorithm requires less than 35 seconds per image on a 2.53GHz Intel Core i3, 2GB RAM system.
This novel system can reliably measure intraretinal sub-layer thickness in both normal eyes and in eyes with DME. These measurements may help characterize patient status and predict prognosis.
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