Segmentation of Preretinal Space in Optical Coherence Tomography Images Using Deep Neural Networks

This paper proposes an efficient segmentation of the preretinal area between the inner limiting membrane (ILM) and posterior cortical vitreous (PCV) of the human eye in an image obtained with the use of optical coherence tomography (OCT). The research was carried out using a database of three-dimens...

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Autores principales:	Agnieszka Stankiewicz, Tomasz Marciniak, Adam Dabrowski, Marcin Stopa, Elzbieta Marciniak, Boguslaw Obara
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	human eye image analysis preretinal space retinal layer segmentation convolutional neural networks UNet optical coherence tomography Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/334d193294474aa190d527ba95eabdd3
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spelling	oai:doaj.org-article:334d193294474aa190d527ba95eabdd32021-11-25T18:57:08ZSegmentation of Preretinal Space in Optical Coherence Tomography Images Using Deep Neural Networks10.3390/s212275211424-8220https://doaj.org/article/334d193294474aa190d527ba95eabdd32021-11-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/22/7521https://doaj.org/toc/1424-8220This paper proposes an efficient segmentation of the preretinal area between the inner limiting membrane (ILM) and posterior cortical vitreous (PCV) of the human eye in an image obtained with the use of optical coherence tomography (OCT). The research was carried out using a database of three-dimensional OCT imaging scans obtained with the Optovue RTVue XR Avanti device. Various types of neural networks (UNet, Attention UNet, ReLayNet, LFUNet) were tested for semantic segmentation, their effectiveness was assessed using the Dice coefficient and compared to the graph theory techniques. Improvement in segmentation efficiency was achieved through the use of relative distance maps. We also show that selecting a larger kernel size for convolutional layers can improve segmentation quality depending on the neural network model. In the case of PVC, we obtain the effectiveness reaching up to <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>96.35</mn><mo>%</mo></mrow></semantics></math></inline-formula>. The proposed solution can be widely used to diagnose vitreomacular traction changes, which is not yet available in scientific or commercial OCT imaging solutions.Agnieszka StankiewiczTomasz MarciniakAdam DabrowskiMarcin StopaElzbieta MarciniakBoguslaw ObaraMDPI AGarticlehuman eye image analysispreretinal spaceretinal layer segmentationconvolutional neural networksUNetoptical coherence tomographyChemical technologyTP1-1185ENSensors, Vol 21, Iss 7521, p 7521 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	human eye image analysis preretinal space retinal layer segmentation convolutional neural networks UNet optical coherence tomography Chemical technology TP1-1185
spellingShingle	human eye image analysis preretinal space retinal layer segmentation convolutional neural networks UNet optical coherence tomography Chemical technology TP1-1185 Agnieszka Stankiewicz Tomasz Marciniak Adam Dabrowski Marcin Stopa Elzbieta Marciniak Boguslaw Obara Segmentation of Preretinal Space in Optical Coherence Tomography Images Using Deep Neural Networks
description	This paper proposes an efficient segmentation of the preretinal area between the inner limiting membrane (ILM) and posterior cortical vitreous (PCV) of the human eye in an image obtained with the use of optical coherence tomography (OCT). The research was carried out using a database of three-dimensional OCT imaging scans obtained with the Optovue RTVue XR Avanti device. Various types of neural networks (UNet, Attention UNet, ReLayNet, LFUNet) were tested for semantic segmentation, their effectiveness was assessed using the Dice coefficient and compared to the graph theory techniques. Improvement in segmentation efficiency was achieved through the use of relative distance maps. We also show that selecting a larger kernel size for convolutional layers can improve segmentation quality depending on the neural network model. In the case of PVC, we obtain the effectiveness reaching up to <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>96.35</mn><mo>%</mo></mrow></semantics></math></inline-formula>. The proposed solution can be widely used to diagnose vitreomacular traction changes, which is not yet available in scientific or commercial OCT imaging solutions.
format	article
author	Agnieszka Stankiewicz Tomasz Marciniak Adam Dabrowski Marcin Stopa Elzbieta Marciniak Boguslaw Obara
author_facet	Agnieszka Stankiewicz Tomasz Marciniak Adam Dabrowski Marcin Stopa Elzbieta Marciniak Boguslaw Obara
author_sort	Agnieszka Stankiewicz
title	Segmentation of Preretinal Space in Optical Coherence Tomography Images Using Deep Neural Networks
title_short	Segmentation of Preretinal Space in Optical Coherence Tomography Images Using Deep Neural Networks
title_full	Segmentation of Preretinal Space in Optical Coherence Tomography Images Using Deep Neural Networks
title_fullStr	Segmentation of Preretinal Space in Optical Coherence Tomography Images Using Deep Neural Networks
title_full_unstemmed	Segmentation of Preretinal Space in Optical Coherence Tomography Images Using Deep Neural Networks
title_sort	segmentation of preretinal space in optical coherence tomography images using deep neural networks
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/334d193294474aa190d527ba95eabdd3
work_keys_str_mv	AT agnieszkastankiewicz segmentationofpreretinalspaceinopticalcoherencetomographyimagesusingdeepneuralnetworks AT tomaszmarciniak segmentationofpreretinalspaceinopticalcoherencetomographyimagesusingdeepneuralnetworks AT adamdabrowski segmentationofpreretinalspaceinopticalcoherencetomographyimagesusingdeepneuralnetworks AT marcinstopa segmentationofpreretinalspaceinopticalcoherencetomographyimagesusingdeepneuralnetworks AT elzbietamarciniak segmentationofpreretinalspaceinopticalcoherencetomographyimagesusingdeepneuralnetworks AT boguslawobara segmentationofpreretinalspaceinopticalcoherencetomographyimagesusingdeepneuralnetworks
_version_	1718410514637258752

Segmentation of Preretinal Space in Optical Coherence Tomography Images Using Deep Neural Networks

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