Abstract
Purpose: :
To compare the performance of segmentation algorithms in the analysis of pigment epithelium detachments (PEDs) using two different spectral domain optical coherence tomography instruments (SDOCT).
Methods: :
Twenty seven eyes from 25 patients with different types of PEDs were enrolled. All eyes were scanned using both the Cirrus SDOCT (Carl Zeiss Meditec, Dublin, CA) and the Spectralis SDOCT (Heidelberg Engineering, Inc., Heidelberg, Germany) instruments during a single visit. All the scans were centered on the fovea. Two different Cirrus scan patterns were used; the 512 x 128 and 200 x 200 A-scan raster pattern covering a 6X6 mm (20x20 degree) area. The Spectralis SDOCT scan pattern consisted of 7 B-scans, each averaged 51 times, covering a 30X5 degree area . The main outcome measure was to compare the segmentation performance of the B-scan which included the foveal center.
Results: :
The Spectralis algorithm failed to properly segment the central B-scan in 25 eyes (92.6%); the algorithm failed to follow the RPE. The Cirrus ialgorithm successfully segmented the RPE layer in every central B-scan. The mean center point thickness recorded by the Spectralis was 362.29 µm ± 156.37 (176-919 µm). The mean center point thickness using the Cirrus was 220.18 µm ± 69.30 (119 - 393 µm) and 223.33 µm ± 70.04 (119-399 µm), using the 200x200 or 512x128 scan patterns, respectively. There was no difference between the two Cirrus scan patterns. There was a statistically significant difference between the Cirrus and Spectralis central thickness measurements (F=15.61; p < 0.001).
Conclusions: :
The Cirrus SDOCT algorithm is superior to the Spectralis algorithm for identifying appropriate segmentation boundaries in the presence PEDs. The Spectralis SDOCT algorithm is unreliable when segmenting PEDS. This leads to erroneous retinal thickness measurements. The Spectralis instrument should only useful for the qualitative assessment of PEDs unless manual adjustments to the boundary layers are performed.
Keywords: imaging/image analysis: clinical • age-related macular degeneration • imaging methods (CT, FA, ICG, MRI, OCT, RTA, SLO, ultrasound)