Automated segmentation of choroidal layers from 3-dimensional macular optical coherence tomography scans
Kyungmoo Lee, Alexis K. Warren, Michael D. Abramoff, Andreas Wahle, S., Scott Whitmore, Ian C. Han, John H. Fingert, Todd E. Scheetz, Robert F., Mullins, Milan Sonka, Elliott H. Sohn

TL;DR
This paper presents a new automated graph-theoretic method for accurately segmenting choroidal layers in 3D OCT scans, demonstrating high repeatability and reproducibility across different imaging platforms.
Contribution
The study introduces a novel automated segmentation approach that outperforms previous methods in accuracy and consistency for measuring choroidal thickness in OCT images.
Findings
Mean DSC of 0.949 indicates high segmentation accuracy.
High reproducibility with ICC of 0.991 across different scans.
Method reliably measures choroidal thickness across OCT platforms.
Abstract
Background: Changes in choroidal thickness are associated with various ocular diseases and the choroid can be imaged using spectral-domain optical coherence tomography (SDOCT) and enhanced depth imaging OCT (EDIOCT). New Method: Eighty macular SDOCT volumes from 80 patients were obtained using the Zeiss Cirrus machine. Eleven additional control subjects had two Cirrus scans done in one visit along with EDIOCT using the Heidelberg Spectralis machine. To automatically segment choroidal layers from the OCT volumes, our graph-theoretic approach was utilized. The segmentation results were compared with reference standards from two graders, and the accuracy of automated segmentation was calculated using unsigned to signed border positioning thickness errors and Dice similarity coefficient (DSC). The repeatability and reproducibility of our choroidal thicknesses were determined by intraclass…
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