Abstract
Glaucomatous damage can be quantified by measuring the thickness of different retinal layers. However, poor image quality may hamper the accuracy of the layer thickness measurement. We determined the effect of poor image quality (low signal-to-noise ratio) on the different layer thicknesses and compared different segmentation algorithms regarding their robustness against this degrading effect. For this purpose, we performed OCT measurements in the macular area of healthy subjects and degraded the image quality by employing neutral density filters. We also analysed OCT scans from glaucoma patients with different disease severity. The algorithms used were: The Canon HS-100's built-in algorithm, DOCTRAP, IOWA, and FWHM, an approach we developed. We showed that the four algorithms used were all susceptible to noise at a varying degree, depending on the retinal layer assessed, and the results between different algorithms were not interchangeable. The algorithms also differed in their ability to differentiate between young healthy eyes and older glaucoma eyes and failed to accurately separate different glaucoma stages from each other.
Highlights
We found a similar layer thinning at 0.3 OD for the IOWA algorithm for both retinal nerve fiber layer (RNFL) and GCIPL at the 5 mm regions of interest (ROIs) used by IOWA
Our results suggest that detecting progression from RNFL, GCIPL, or total retinal thickness (TRT) in either ROIs with any of the algorithms is challenging
Our findings suggest that TRT is both sensitive to disease induced changes and robust against noise, apparently outperforming mRNFL and GCIPL, and could be a viable measure of glaucomatous damage
Summary
The primary site involved in this disease consists of the retinal ganglion cells (RGCs). Loss of RGC axons causes excavation of the optic nerve head (ONH) and thinning of the retinal nerve fiber layer (RNFL). In order to measure the thickness of these retinal layers in vivo, the currently most commonly used technique is optical coherence tomography (OCT) [1]. It has been shown that OCT-reported layer thicknesses depend largely on image quality; a lower image quality reduces the observed thickness [2]. This underestimation of layer thickness hampers the usage of OCT both for the detection of glaucoma (screening) and for progression detection, once glaucoma has been diagnosed (monitoring)
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