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Proc SPIE Int Soc Opt Eng. 2017 Feb 11;10137. pii: 101371M. doi: 10.1117/12.2254849. Epub 2017 Mar 13.

Improving graph-based OCT segmentation for severe pathology in Retinitis Pigmentosa patients.

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Dept. of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218, US.
Dept. of Computer Science, The Johns Hopkins University, Baltimore, MD 21218, USA.
Nova Southeastern University, College of Optometry, Ft. Lauderdale, FL 33314, USA.
Dept. of Ophthalmology, Boston University School Of Medicine, Boston, MA 02118, USA.


Three dimensional segmentation of macular optical coherence tomography (OCT) data of subjects with retinitis pigmentosa (RP) is a challenging problem due to the disappearance of the photoreceptor layers, which causes algorithms developed for segmentation of healthy data to perform poorly on RP patients. In this work, we present enhancements to a previously developed graph-based OCT segmentation pipeline to enable processing of RP data. The algorithm segments eight retinal layers in RP data by relaxing constraints on the thickness and smoothness of each layer learned from healthy data. Following from prior work, a random forest classifier is first trained on the RP data to estimate boundary probabilities, which are used by a graph search algorithm to find the optimal set of nine surfaces that fit the data. Due to the intensity disparity between normal layers of healthy controls and layers in various stages of degeneration in RP patients, an additional intensity normalization step is introduced. Leave-one-out validation on data acquired from nine subjects showed an average overall boundary error of 4.22 μm as compared to 6.02 μm using the original algorithm.


OCT; random forest; retina; retinitis pigmentosa; segmentation

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