In Vivo Photoacoustic Imaging of Anterior Ocular Vasculature: A Random Sample Consensus Approach

Abstract Visualizing ocular vasculature is important in clinical ophthalmology because ocular circulation abnormalities are early signs of ocular diseases. Photoacoustic microscopy (PAM) images the ocular vasculature without using exogenous contrast agents, avoiding associated side effects. Moreover...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Seungwan Jeon, Hyun Beom Song, Jaewoo Kim, Byung Joo Lee, Ravi Managuli, Jin Hyoung Kim, Jeong Hun Kim, Chulhong Kim
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/381d46c703544ce0a09b83cf33530dfe
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:381d46c703544ce0a09b83cf33530dfe
record_format	dspace
spelling	oai:doaj.org-article:381d46c703544ce0a09b83cf33530dfe2021-12-02T11:40:41ZIn Vivo Photoacoustic Imaging of Anterior Ocular Vasculature: A Random Sample Consensus Approach10.1038/s41598-017-04334-z2045-2322https://doaj.org/article/381d46c703544ce0a09b83cf33530dfe2017-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-04334-zhttps://doaj.org/toc/2045-2322Abstract Visualizing ocular vasculature is important in clinical ophthalmology because ocular circulation abnormalities are early signs of ocular diseases. Photoacoustic microscopy (PAM) images the ocular vasculature without using exogenous contrast agents, avoiding associated side effects. Moreover, 3D PAM images can be useful in understanding vessel-related eye disease. However, the complex structure of the multi-layered vessels still present challenges in evaluating ocular vasculature. In this study, we demonstrate a new method to evaluate blood circulation in the eye by combining in vivo PAM imaging and an ocular surface estimation method based on a machine learning algorithm: a random sample consensus algorithm. By using the developed estimation method, we were able to visualize the PA ocular vascular image intuitively and demonstrate layer-by-layer analysis of injured ocular vasculature. We believe that our method can provide more accurate evaluations of the eye circulation in ophthalmic applications.Seungwan JeonHyun Beom SongJaewoo KimByung Joo LeeRavi ManaguliJin Hyoung KimJeong Hun KimChulhong KimNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-9 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Seungwan Jeon Hyun Beom Song Jaewoo Kim Byung Joo Lee Ravi Managuli Jin Hyoung Kim Jeong Hun Kim Chulhong Kim In Vivo Photoacoustic Imaging of Anterior Ocular Vasculature: A Random Sample Consensus Approach
description	Abstract Visualizing ocular vasculature is important in clinical ophthalmology because ocular circulation abnormalities are early signs of ocular diseases. Photoacoustic microscopy (PAM) images the ocular vasculature without using exogenous contrast agents, avoiding associated side effects. Moreover, 3D PAM images can be useful in understanding vessel-related eye disease. However, the complex structure of the multi-layered vessels still present challenges in evaluating ocular vasculature. In this study, we demonstrate a new method to evaluate blood circulation in the eye by combining in vivo PAM imaging and an ocular surface estimation method based on a machine learning algorithm: a random sample consensus algorithm. By using the developed estimation method, we were able to visualize the PA ocular vascular image intuitively and demonstrate layer-by-layer analysis of injured ocular vasculature. We believe that our method can provide more accurate evaluations of the eye circulation in ophthalmic applications.
format	article
author	Seungwan Jeon Hyun Beom Song Jaewoo Kim Byung Joo Lee Ravi Managuli Jin Hyoung Kim Jeong Hun Kim Chulhong Kim
author_facet	Seungwan Jeon Hyun Beom Song Jaewoo Kim Byung Joo Lee Ravi Managuli Jin Hyoung Kim Jeong Hun Kim Chulhong Kim
author_sort	Seungwan Jeon
title	In Vivo Photoacoustic Imaging of Anterior Ocular Vasculature: A Random Sample Consensus Approach
title_short	In Vivo Photoacoustic Imaging of Anterior Ocular Vasculature: A Random Sample Consensus Approach
title_full	In Vivo Photoacoustic Imaging of Anterior Ocular Vasculature: A Random Sample Consensus Approach
title_fullStr	In Vivo Photoacoustic Imaging of Anterior Ocular Vasculature: A Random Sample Consensus Approach
title_full_unstemmed	In Vivo Photoacoustic Imaging of Anterior Ocular Vasculature: A Random Sample Consensus Approach
title_sort	in vivo photoacoustic imaging of anterior ocular vasculature: a random sample consensus approach
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/381d46c703544ce0a09b83cf33530dfe
work_keys_str_mv	AT seungwanjeon invivophotoacousticimagingofanteriorocularvasculaturearandomsampleconsensusapproach AT hyunbeomsong invivophotoacousticimagingofanteriorocularvasculaturearandomsampleconsensusapproach AT jaewookim invivophotoacousticimagingofanteriorocularvasculaturearandomsampleconsensusapproach AT byungjoolee invivophotoacousticimagingofanteriorocularvasculaturearandomsampleconsensusapproach AT ravimanaguli invivophotoacousticimagingofanteriorocularvasculaturearandomsampleconsensusapproach AT jinhyoungkim invivophotoacousticimagingofanteriorocularvasculaturearandomsampleconsensusapproach AT jeonghunkim invivophotoacousticimagingofanteriorocularvasculaturearandomsampleconsensusapproach AT chulhongkim invivophotoacousticimagingofanteriorocularvasculaturearandomsampleconsensusapproach
_version_	1718395574166749184

In Vivo Photoacoustic Imaging of Anterior Ocular Vasculature: A Random Sample Consensus Approach

Ejemplares similares