@inproceedings{fc5b7a9a7843424a8ba1ee86c271be60,
title = "Improving graph-based OCT segmentation for severe pathology in retinitis pigmentosa patients",
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.",
keywords = "OCT, Random forest, Retina, Retinitis pigmentosa, Segmentation",
author = "Andrew Lang and Aaron Carass and Bittner, {Ava K.} and Howard Ying and Prince, {Jerry L.}",
note = "Publisher Copyright: {\textcopyright} 2017 SPIE.; Medical Imaging 2017: Biomedical Applications in Molecular, Structural, and Functional Imaging ; Conference date: 12-02-2017 Through 14-02-2017",
year = "2017",
doi = "10.1117/12.2254849",
language = "English (US)",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
publisher = "SPIE",
editor = "Barjor Gimi and Andrzej Krol",
booktitle = "Medical Imaging 2017",
}