title: A combined machine-learning and graph-based framework for the segmentation of retinal surfaces in SD-OCT volumes.
abstract: Optical coherence tomography is routinely used clinically for the detection and management of ocular diseases as well as in research where the studies may involve animals. This routine use requires that the developed automated segmentation methods not only be accurate and reliable, but also be adaptable to meet new requirements. We have previously proposed the use of a graph-theoretic approach for the automated 3-D segmentation of multiple retinal surfaces in volumetric human SD-OCT scans. The method ensures the global optimality of the set of surfaces with respect to a cost function. Cost functions have thus far been typically designed by hand by domain experts. This difficult and time-consuming task significantly impacts the adaptability of these methods to new models. Here, we describe a framework for the automated machine-learning based design of the cost function utilized by this graph-theoretic method. The impact of the learned components on the final segmentation accuracy are statistically assessed in order to tailor the method to specific applications. This adaptability is demonstrated by utilizing the method to segment seven, ten and five retinal surfaces from SD-OCT scans obtained from humans, mice and canines, respectively. The overall unsigned border position errors observed when using the recommended configuration of the graph-theoretic method was 6.45 ± 1.87 μm, 3.35 ± 0.62 μm and 9.75 ± 3.18 μm for the human, mouse and canine set of images, respectively.
authors.affiliation: Dept. of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA, USA.
authors.forename: Bhavna J
authors.initials: BJ
authors.lastname: Antony
authors.affiliation: Dept. of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA, USA ; Dept. of Ophthalmology and Visual Sciences, The University of Iowa, Iowa City, IA, USA ; Iowa City VA Healthcare System, Iowa City, IA, USA ; Dept. of Biomedical Engineering, The University of Iowa, Iowa City, IA, USA ; The Stephen A. Wynn Institute for Vision Research, Iowa City, IA, USA.
authors.forename: Michael D
authors.initials: MD
authors.lastname: Abràmoff
authors.affiliation: Dept. of Ophthalmology and Visual Sciences, The University of Iowa, Iowa City, IA, USA ; Iowa City VA Healthcare System, Iowa City, IA, USA.
authors.forename: Matthew M
authors.initials: MM
authors.lastname: Harper
authors.affiliation: Dept. of Ophthalmology and Visual Sciences, The University of Iowa, Iowa City, IA, USA ; Department of Ophthalmology, Dong-A University, College of Medicine and Medical Research Center, Busan, South Korea.
authors.forename: Woojin
authors.initials: W
authors.lastname: Jeong
authors.affiliation: Dept. of Ophthalmology and Visual Sciences, The University of Iowa, Iowa City, IA, USA ; The Stephen A. Wynn Institute for Vision Research, Iowa City, IA, USA.
authors.forename: Elliott H
authors.initials: EH
authors.lastname: Sohn
authors.affiliation: Dept. of Ophthalmology and Visual Sciences, The University of Iowa, Iowa City, IA, USA.
authors.forename: Young H
authors.initials: YH
authors.lastname: Kwon
authors.affiliation: Dept. of Ophthalmology and Visual Sciences, The University of Iowa, Iowa City, IA, USA ; Iowa City VA Healthcare System, Iowa City, IA, USA.
authors.forename: Randy
authors.initials: R
authors.lastname: Kardon
authors.affiliation: Dept. of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA, USA ; Iowa City VA Healthcare System, Iowa City, IA, USA.
authors.forename: Mona K
authors.initials: MK
authors.lastname: Garvin
keywords: (100.0100) Image processing; (100.2000) Digital image processing; (100.4994) Pattern recognition, image transforms; (100.6890) Three-dimensional image processing; (110.4500) Optical coherence tomography