Automated choroidal neovascularization detection algorithm for optical coherence tomography angiography

Li Liu, Simon S. Gao, Steven Bailey, David Huang, Dengwang Li, Jia Yali

Research output: Contribution to journalArticle

51 Citations (Scopus)

Abstract

Optical coherence tomography angiography has recently been used to visualize choroidal neovascularization (CNV) in participants with age-related macular degeneration. Identification and quantification of CNV area is important clinically for disease assessment. An automated algorithm for CNV area detection is presented in this article. It relies on denoising and a saliency detection model to overcome issues such as projection artifacts and the heterogeneity of CNV. Qualitative and quantitative evaluations were performed on scans of 7 participants. Results from the algorithm agreed well with manual delineation of CNV area.

Original languageEnglish (US)
Article numberA018
Pages (from-to)3564-3575
Number of pages12
JournalBiomedical Optics Express
Volume6
Issue number9
DOIs
StatePublished - Aug 25 2015

Fingerprint

angiogenesis
Choroidal Neovascularization
angiography
Optical Coherence Tomography
Angiography
tomography
degeneration
delineation
Macular Degeneration
Artifacts
artifacts
projection
evaluation

ASJC Scopus subject areas

  • Atomic and Molecular Physics, and Optics
  • Biotechnology

Cite this

Automated choroidal neovascularization detection algorithm for optical coherence tomography angiography. / Liu, Li; Gao, Simon S.; Bailey, Steven; Huang, David; Li, Dengwang; Yali, Jia.

In: Biomedical Optics Express, Vol. 6, No. 9, A018, 25.08.2015, p. 3564-3575.

Research output: Contribution to journalArticle

@article{72a83320896e495cb4f92e194c400ff1,
title = "Automated choroidal neovascularization detection algorithm for optical coherence tomography angiography",
abstract = "Optical coherence tomography angiography has recently been used to visualize choroidal neovascularization (CNV) in participants with age-related macular degeneration. Identification and quantification of CNV area is important clinically for disease assessment. An automated algorithm for CNV area detection is presented in this article. It relies on denoising and a saliency detection model to overcome issues such as projection artifacts and the heterogeneity of CNV. Qualitative and quantitative evaluations were performed on scans of 7 participants. Results from the algorithm agreed well with manual delineation of CNV area.",
author = "Li Liu and Gao, {Simon S.} and Steven Bailey and David Huang and Dengwang Li and Jia Yali",
year = "2015",
month = "8",
day = "25",
doi = "10.1364/BOE.6.003564",
language = "English (US)",
volume = "6",
pages = "3564--3575",
journal = "Biomedical Optics Express",
issn = "2156-7085",
publisher = "The Optical Society",
number = "9",

}

TY - JOUR

T1 - Automated choroidal neovascularization detection algorithm for optical coherence tomography angiography

AU - Liu, Li

AU - Gao, Simon S.

AU - Bailey, Steven

AU - Huang, David

AU - Li, Dengwang

AU - Yali, Jia

PY - 2015/8/25

Y1 - 2015/8/25

N2 - Optical coherence tomography angiography has recently been used to visualize choroidal neovascularization (CNV) in participants with age-related macular degeneration. Identification and quantification of CNV area is important clinically for disease assessment. An automated algorithm for CNV area detection is presented in this article. It relies on denoising and a saliency detection model to overcome issues such as projection artifacts and the heterogeneity of CNV. Qualitative and quantitative evaluations were performed on scans of 7 participants. Results from the algorithm agreed well with manual delineation of CNV area.

AB - Optical coherence tomography angiography has recently been used to visualize choroidal neovascularization (CNV) in participants with age-related macular degeneration. Identification and quantification of CNV area is important clinically for disease assessment. An automated algorithm for CNV area detection is presented in this article. It relies on denoising and a saliency detection model to overcome issues such as projection artifacts and the heterogeneity of CNV. Qualitative and quantitative evaluations were performed on scans of 7 participants. Results from the algorithm agreed well with manual delineation of CNV area.

UR - http://www.scopus.com/inward/record.url?scp=84946044466&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84946044466&partnerID=8YFLogxK

U2 - 10.1364/BOE.6.003564

DO - 10.1364/BOE.6.003564

M3 - Article

AN - SCOPUS:84946044466

VL - 6

SP - 3564

EP - 3575

JO - Biomedical Optics Express

JF - Biomedical Optics Express

SN - 2156-7085

IS - 9

M1 - A018

ER -