Evaluation of focus and deep learning methods for automated image grading and factors influencing image quality in adaptive optics ophthalmoscopy

Abstract Adaptive optics flood illumination ophthalmoscopy (AO-FIO) is an established imaging tool in the investigation of retinal diseases. However, the clinical interpretation of AO-FIO images can be challenging due to varied image quality. Therefore, image quality assessment is essential before i...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Danuta M. Sampson, David Alonso-Caneiro, Avenell L. Chew, Jonathan La, Danial Roshandel, Yufei Wang, Jane C. Khan, Enid Chelva, Paul G. Stevenson, Fred K. Chen
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/0ccb52b2c5894359be829a0dd7279537
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:0ccb52b2c5894359be829a0dd7279537
record_format	dspace
spelling	oai:doaj.org-article:0ccb52b2c5894359be829a0dd72795372021-12-02T18:51:46ZEvaluation of focus and deep learning methods for automated image grading and factors influencing image quality in adaptive optics ophthalmoscopy10.1038/s41598-021-96068-22045-2322https://doaj.org/article/0ccb52b2c5894359be829a0dd72795372021-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-96068-2https://doaj.org/toc/2045-2322Abstract Adaptive optics flood illumination ophthalmoscopy (AO-FIO) is an established imaging tool in the investigation of retinal diseases. However, the clinical interpretation of AO-FIO images can be challenging due to varied image quality. Therefore, image quality assessment is essential before interpretation. An image assessment tool will also assist further work on improving the image quality, either during acquisition or post processing. In this paper, we describe, validate and compare two automated image quality assessment methods; the energy of Laplacian focus operator (LAPE; not commonly used but easily implemented) and convolutional neural network (CNN; effective but more complex approach). We also evaluate the effects of subject age, axial length, refractive error, fixation stability, disease status and retinal location on AO-FIO image quality. Based on analysis of 10,250 images of 50 × 50 μm size, at 41 retinal locations, from 50 subjects we demonstrate that CNN slightly outperforms LAPE in image quality assessment. CNN achieves accuracy of 89%, whereas LAPE metric achieves 73% and 80% (for a linear regression and random forest multiclass classifier methods, respectively) compared to ground truth. Furthermore, the retinal location, age and disease are factors that can influence the likelihood of poor image quality.Danuta M. SampsonDavid Alonso-CaneiroAvenell L. ChewJonathan LaDanial RoshandelYufei WangJane C. KhanEnid ChelvaPaul G. StevensonFred K. ChenNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Danuta M. Sampson David Alonso-Caneiro Avenell L. Chew Jonathan La Danial Roshandel Yufei Wang Jane C. Khan Enid Chelva Paul G. Stevenson Fred K. Chen Evaluation of focus and deep learning methods for automated image grading and factors influencing image quality in adaptive optics ophthalmoscopy
description	Abstract Adaptive optics flood illumination ophthalmoscopy (AO-FIO) is an established imaging tool in the investigation of retinal diseases. However, the clinical interpretation of AO-FIO images can be challenging due to varied image quality. Therefore, image quality assessment is essential before interpretation. An image assessment tool will also assist further work on improving the image quality, either during acquisition or post processing. In this paper, we describe, validate and compare two automated image quality assessment methods; the energy of Laplacian focus operator (LAPE; not commonly used but easily implemented) and convolutional neural network (CNN; effective but more complex approach). We also evaluate the effects of subject age, axial length, refractive error, fixation stability, disease status and retinal location on AO-FIO image quality. Based on analysis of 10,250 images of 50 × 50 μm size, at 41 retinal locations, from 50 subjects we demonstrate that CNN slightly outperforms LAPE in image quality assessment. CNN achieves accuracy of 89%, whereas LAPE metric achieves 73% and 80% (for a linear regression and random forest multiclass classifier methods, respectively) compared to ground truth. Furthermore, the retinal location, age and disease are factors that can influence the likelihood of poor image quality.
format	article
author	Danuta M. Sampson David Alonso-Caneiro Avenell L. Chew Jonathan La Danial Roshandel Yufei Wang Jane C. Khan Enid Chelva Paul G. Stevenson Fred K. Chen
author_facet	Danuta M. Sampson David Alonso-Caneiro Avenell L. Chew Jonathan La Danial Roshandel Yufei Wang Jane C. Khan Enid Chelva Paul G. Stevenson Fred K. Chen
author_sort	Danuta M. Sampson
title	Evaluation of focus and deep learning methods for automated image grading and factors influencing image quality in adaptive optics ophthalmoscopy
title_short	Evaluation of focus and deep learning methods for automated image grading and factors influencing image quality in adaptive optics ophthalmoscopy
title_full	Evaluation of focus and deep learning methods for automated image grading and factors influencing image quality in adaptive optics ophthalmoscopy
title_fullStr	Evaluation of focus and deep learning methods for automated image grading and factors influencing image quality in adaptive optics ophthalmoscopy
title_full_unstemmed	Evaluation of focus and deep learning methods for automated image grading and factors influencing image quality in adaptive optics ophthalmoscopy
title_sort	evaluation of focus and deep learning methods for automated image grading and factors influencing image quality in adaptive optics ophthalmoscopy
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/0ccb52b2c5894359be829a0dd7279537
work_keys_str_mv	AT danutamsampson evaluationoffocusanddeeplearningmethodsforautomatedimagegradingandfactorsinfluencingimagequalityinadaptiveopticsophthalmoscopy AT davidalonsocaneiro evaluationoffocusanddeeplearningmethodsforautomatedimagegradingandfactorsinfluencingimagequalityinadaptiveopticsophthalmoscopy AT avenelllchew evaluationoffocusanddeeplearningmethodsforautomatedimagegradingandfactorsinfluencingimagequalityinadaptiveopticsophthalmoscopy AT jonathanla evaluationoffocusanddeeplearningmethodsforautomatedimagegradingandfactorsinfluencingimagequalityinadaptiveopticsophthalmoscopy AT danialroshandel evaluationoffocusanddeeplearningmethodsforautomatedimagegradingandfactorsinfluencingimagequalityinadaptiveopticsophthalmoscopy AT yufeiwang evaluationoffocusanddeeplearningmethodsforautomatedimagegradingandfactorsinfluencingimagequalityinadaptiveopticsophthalmoscopy AT janeckhan evaluationoffocusanddeeplearningmethodsforautomatedimagegradingandfactorsinfluencingimagequalityinadaptiveopticsophthalmoscopy AT enidchelva evaluationoffocusanddeeplearningmethodsforautomatedimagegradingandfactorsinfluencingimagequalityinadaptiveopticsophthalmoscopy AT paulgstevenson evaluationoffocusanddeeplearningmethodsforautomatedimagegradingandfactorsinfluencingimagequalityinadaptiveopticsophthalmoscopy AT fredkchen evaluationoffocusanddeeplearningmethodsforautomatedimagegradingandfactorsinfluencingimagequalityinadaptiveopticsophthalmoscopy
_version_	1718377369528434688

Evaluation of focus and deep learning methods for automated image grading and factors influencing image quality in adaptive optics ophthalmoscopy

Ejemplares similares