Retinal layer segmentation of macular OCT images using boundary classification

Andrew Lang, Aaron Carass, Matthew Hauser, Elias S. Sotirchos, Peter Calabresi, Howard S. Ying, Jerry Ladd Prince

Research output: Contribution to journalArticle

Abstract

Optical coherence tomography (OCT) has proven to be an essential imaging modality for ophthalmology and is proving to be very important in neurology. OCT enables high resolution imaging of the retina, both at the optic nerve head and the macula. Macular retinal layer thicknesses provide useful diagnostic information and have been shown to correlate well with measures of disease severity in several diseases. Since manual segmentation of these layers is time consuming and prone to bias, automatic segmentation methods are critical for full utilization of this technology. In this work, we build a random forest classifier to segment eight retinal layers in macular cube images acquired by OCT. The random forest classifier learns the boundary pixels between layers, producing an accurate probability map for each boundary, which is then processed to finalize the boundaries. Using this algorithm, we can accurately segment the entire retina contained in the macular cube to an accuracy of at least 4.3 microns for any of the nine boundaries. Experiments were carried out on both healthy and multiple sclerosis subjects, with no difference in the accuracy of our algorithm found between the groups.

Original languageEnglish (US)
Pages (from-to)1133-1152
Number of pages20
JournalBiomedical Optics Express
Volume4
Issue number7
DOIs
StatePublished - 2013

Fingerprint

Optical Coherence Tomography
tomography
Retina
retina
classifiers
Optic Disk
Ophthalmology
ophthalmology
Neurology
neurology
Multiple Sclerosis
nerves
Technology
pixels
optics
high resolution

ASJC Scopus subject areas

  • Atomic and Molecular Physics, and Optics
  • Biotechnology

Cite this

Retinal layer segmentation of macular OCT images using boundary classification. / Lang, Andrew; Carass, Aaron; Hauser, Matthew; Sotirchos, Elias S.; Calabresi, Peter; Ying, Howard S.; Prince, Jerry Ladd.

In: Biomedical Optics Express, Vol. 4, No. 7, 2013, p. 1133-1152.

Research output: Contribution to journalArticle

Lang, Andrew ; Carass, Aaron ; Hauser, Matthew ; Sotirchos, Elias S. ; Calabresi, Peter ; Ying, Howard S. ; Prince, Jerry Ladd. / Retinal layer segmentation of macular OCT images using boundary classification. In: Biomedical Optics Express. 2013 ; Vol. 4, No. 7. pp. 1133-1152.
@article{8e6d98f5050146bdbaad2671f409c1e3,
title = "Retinal layer segmentation of macular OCT images using boundary classification",
abstract = "Optical coherence tomography (OCT) has proven to be an essential imaging modality for ophthalmology and is proving to be very important in neurology. OCT enables high resolution imaging of the retina, both at the optic nerve head and the macula. Macular retinal layer thicknesses provide useful diagnostic information and have been shown to correlate well with measures of disease severity in several diseases. Since manual segmentation of these layers is time consuming and prone to bias, automatic segmentation methods are critical for full utilization of this technology. In this work, we build a random forest classifier to segment eight retinal layers in macular cube images acquired by OCT. The random forest classifier learns the boundary pixels between layers, producing an accurate probability map for each boundary, which is then processed to finalize the boundaries. Using this algorithm, we can accurately segment the entire retina contained in the macular cube to an accuracy of at least 4.3 microns for any of the nine boundaries. Experiments were carried out on both healthy and multiple sclerosis subjects, with no difference in the accuracy of our algorithm found between the groups.",
author = "Andrew Lang and Aaron Carass and Matthew Hauser and Sotirchos, {Elias S.} and Peter Calabresi and Ying, {Howard S.} and Prince, {Jerry Ladd}",
year = "2013",
doi = "10.1364/BOE.4.001133",
language = "English (US)",
volume = "4",
pages = "1133--1152",
journal = "Biomedical Optics Express",
issn = "2156-7085",
publisher = "The Optical Society",
number = "7",

}

TY - JOUR

T1 - Retinal layer segmentation of macular OCT images using boundary classification

AU - Lang, Andrew

AU - Carass, Aaron

AU - Hauser, Matthew

AU - Sotirchos, Elias S.

AU - Calabresi, Peter

AU - Ying, Howard S.

AU - Prince, Jerry Ladd

PY - 2013

Y1 - 2013

N2 - Optical coherence tomography (OCT) has proven to be an essential imaging modality for ophthalmology and is proving to be very important in neurology. OCT enables high resolution imaging of the retina, both at the optic nerve head and the macula. Macular retinal layer thicknesses provide useful diagnostic information and have been shown to correlate well with measures of disease severity in several diseases. Since manual segmentation of these layers is time consuming and prone to bias, automatic segmentation methods are critical for full utilization of this technology. In this work, we build a random forest classifier to segment eight retinal layers in macular cube images acquired by OCT. The random forest classifier learns the boundary pixels between layers, producing an accurate probability map for each boundary, which is then processed to finalize the boundaries. Using this algorithm, we can accurately segment the entire retina contained in the macular cube to an accuracy of at least 4.3 microns for any of the nine boundaries. Experiments were carried out on both healthy and multiple sclerosis subjects, with no difference in the accuracy of our algorithm found between the groups.

AB - Optical coherence tomography (OCT) has proven to be an essential imaging modality for ophthalmology and is proving to be very important in neurology. OCT enables high resolution imaging of the retina, both at the optic nerve head and the macula. Macular retinal layer thicknesses provide useful diagnostic information and have been shown to correlate well with measures of disease severity in several diseases. Since manual segmentation of these layers is time consuming and prone to bias, automatic segmentation methods are critical for full utilization of this technology. In this work, we build a random forest classifier to segment eight retinal layers in macular cube images acquired by OCT. The random forest classifier learns the boundary pixels between layers, producing an accurate probability map for each boundary, which is then processed to finalize the boundaries. Using this algorithm, we can accurately segment the entire retina contained in the macular cube to an accuracy of at least 4.3 microns for any of the nine boundaries. Experiments were carried out on both healthy and multiple sclerosis subjects, with no difference in the accuracy of our algorithm found between the groups.

UR - http://www.scopus.com/inward/record.url?scp=84879267573&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84879267573&partnerID=8YFLogxK

U2 - 10.1364/BOE.4.001133

DO - 10.1364/BOE.4.001133

M3 - Article

VL - 4

SP - 1133

EP - 1152

JO - Biomedical Optics Express

JF - Biomedical Optics Express

SN - 2156-7085

IS - 7

ER -