Computer Vision Techniques Applied for Diagnostic Analysis of Retinal OCT Images: A Review

Usman, Muhammad; Fraz, Muhammad Moazam; Barman, Sarah

doi:10.1007/s11831-016-9174-3

Cited by 18 publications

(7 citation statements)

References 96 publications

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“…Macula an oval‐shaped retina's portion covers 5500 microns, with the midpoint at the fovea and is responsible for focused vision. Its disorders lead to macular diseases that outcome in vision loss [12]. Nathan M. Radcliffe et al evaluated retinal nerve fibre layer (RNFL) thinning using SD‐OCT to detect the changes and loss of 30% portion of the macula, i.e.…”

Section: Literature Reviewmentioning

confidence: 99%

Automated glaucoma detection using retinal layers segmentation and optic cup‐to‐disc ratio in optical coherence tomography images

Ramzan

Akram

Shaukat

et al. 2019

IET Image Processing

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Section: Literature Reviewmentioning

confidence: 99%

Automated glaucoma detection using retinal layers segmentation and optic cup‐to‐disc ratio in optical coherence tomography images

Ramzan

Akram

Shaukat

et al. 2019

IET Image Processing

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“…Glaucoma is the leading cause of irreversible blindness [1], and the early detection of glaucoma is of great significance to cure this disease. Elevated intraocular pressure (IOP), visual field (VF) defect and glaucomatous optic neuropathy (GON) yield three main clinical symptoms for glaucoma diagnosis [2]. Retinal nerve fiber layer (RNFL) thinning is an early signal of glaucoma [2].…”

Section: Introductionmentioning

confidence: 99%

“…Elevated intraocular pressure (IOP), visual field (VF) defect and glaucomatous optic neuropathy (GON) yield three main clinical symptoms for glaucoma diagnosis [2]. Retinal nerve fiber layer (RNFL) thinning is an early signal of glaucoma [2]. The advantages of Optical Coherence Tomography (OCT), such as fast scanning speed, non-invasiveness, high resolution and repeatability, make it widely used in eye disease diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Joint retina segmentation and classification for early glaucoma diagnosis

Wang

et al. 2019

Biomed. Opt. Express

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We propose a joint segmentation and classification deep model for early glaucoma diagnosis using retina imaging with optical coherence tomography (OCT). Our motivation roots in the observation that ophthalmologists make the clinical decision by analyzing the retinal nerve fiber layer (RNFL) from OCT images. To simulate this process, we propose a novel deep model that joins the retinal layer segmentation and glaucoma classification. Our model consists of three parts. First, the segmentation network simultaneously predicts both six retinal layers and five boundaries between them. Then, we introduce a post processing algorithm to fuse the two results while enforcing the topology correctness. Finally, the classification network takes the RNFL thickness vector as input and outputs the probability of being glaucoma. In the classification network, we propose a carefully designed module to implement the clinical strategy to diagnose glaucoma. We validate our method both in a collected dataset of 1004 circular OCT B-Scans from 234 subjects and in a public dataset of 110 B-Scans from 10 patients with diabetic macular edema. Experimental results demonstrate that our method achieves superior segmentation performance than other state-of-the-art methods both in our collected dataset and in public dataset with severe retina pathology. For glaucoma classification, our model achieves diagnostic accuracy of 81.4% with AUC of 0.864, which clearly outperforms baseline methods.

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“…No campo oftalmológico, foram desenvolvidos um grande número de sistemas de Diagnóstico Auxiliado por Computador (CAD -Computer Aided Diagnosis) para o auxílio na detecção de vários tipos de doenças oculares, entre elas o glaucoma [1]. Estes sistemas têm potencial para fornecer uma solução alternativa aos programas de triagem em massa, que precisam examinar um grande número de imagens de fundo de olho de forma eficiente e robusta.…”

Section: Introductionunclassified

Diagnóstico de Glaucoma Utilizando Atributos de Textura e CNN’s Pré-treinadas

Claro

Veras

Santana

et al. 2018

RITA

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Resumo: Glaucoma is a disease that damages the optic nerve. It is considered the second leading cause of blindness in the world. Several automatic diagnostic systems have been proposed. However, such systems have not been shown to be able to handle a great diversity of images. Thus, this work proposes a method of detecting glaucoma, through the use of texture descriptors and Convolutional Neural Networks (CNNs). Tests were conducted in four public databases, making a total of 873 images. The results showed that the junction of GLCM and pre-trained CNN descriptors and the use of the Random Forest classifier are promising in the detection of this pathology, obtaining an accuracy of 91.06%. Keywords: medical images -glaucoma diagnosis -texture features -transfer learning Resumo: Glaucomaé uma doença que danifica o nervoóptico. Elaé considerada a segunda principal causa de cegueira no mundo. Vários sistemas de diagnóstico automático têm sido propostos, contudo, tais sistemas não demonstraram ser capazes de lidar com uma grande diversidade de imagens. Dessa forma, este trabalho propõe um método de detecção do glaucoma, através do uso de descritores de textura e Redes Neurais Convolucionais (CNNs). Testes foram conduzidos em quatro bases públicas, o que constitui um total de 873 imagens. Os resultados mostraram que a junção dos descritores GLCM e CNNs pré-treinadas e a utilização do classificador Random Forest são promissores na detecção dessa patologia, obtendo uma acurácia de 91,06%.. Palavras-Chave: imagens médicas -diagnóstico de glaucoma -atributos de textura -transferência de aprendizagem

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Computer Vision Techniques Applied for Diagnostic Analysis of Retinal OCT Images: A Review

Cited by 18 publications

References 96 publications

Automated glaucoma detection using retinal layers segmentation and optic cup‐to‐disc ratio in optical coherence tomography images

Automated glaucoma detection using retinal layers segmentation and optic cup‐to‐disc ratio in optical coherence tomography images

Joint retina segmentation and classification for early glaucoma diagnosis

Diagnóstico de Glaucoma Utilizando Atributos de Textura e CNN’s Pré-treinadas

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