Improving graph-based OCT segmentation for severe pathology in retinitis pigmentosa patients

Andrew Lang; Aaron Carass; Ava K. Bittner; Howard S. Ying; Jerry L. Prince

doi:10.1117/12.2254849

13 March 2017 Improving graph-based OCT segmentation for severe pathology in retinitis pigmentosa patients

Andrew Lang, Aaron Carass, Ava K. Bittner, Howard S. Ying, Jerry L. Prince

Proceedings Volume 10137, Medical Imaging 2017: Biomedical Applications in Molecular, Structural, and Functional Imaging; 101371M (2017) https://doi.org/10.1117/12.2254849
Event: SPIE Medical Imaging, 2017, Orlando, Florida, United States

Abstract

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.

Citation Download Citation

Andrew Lang, Aaron Carass, Ava K. Bittner, Howard S. Ying, and Jerry L. Prince "Improving graph-based OCT segmentation for severe pathology in retinitis pigmentosa patients", Proc. SPIE 10137, Medical Imaging 2017: Biomedical Applications in Molecular, Structural, and Functional Imaging, 101371M (13 March 2017); https://doi.org/10.1117/12.2254849

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