Detection of retinal disorders from OCT images using generative adversarial networks

Smitha, A.; Jidesh, P.

doi:10.1007/s11042-022-12475-1

Cited by 9 publications

(5 citation statements)

References 45 publications

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“…They were not segmented in the retinal fluid regions. In addition, the prediction of the model can be enhanced by employing attention modules that are capable of transferring crucial data across multiple layers of the deep learning model 41 …”

Section: Resultsmentioning

confidence: 99%

“…In addition, the prediction of the model can be enhanced by employing attention modules that are capable of transferring crucial data across multiple layers of the deep learning model. 41 The attention modules of the AR U-Net++ model have been used to improve the accuracy of the segmentation and to focus on a particular region. In addition, this model provides better prediction results when compared to the existing models available at that particular period of time.…”

Section: Discussionmentioning

confidence: 99%

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Application program interface for automatic segmentation of retinal layers and fluids in Optical Coherence Tomography ‐ Neovascular Age related Macular degeneration retinal images using deep learning models

Jeya Prabha,

Sameera Fathimal,

Meghana

et al. 2023

Int J Imaging Syst Tech

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Optical coherence tomography is a non‐invasive imaging technique that provides micrometer‐resolution images of retinal structures. These images can assist in identifying changes under the retina's surface, such as edema. This study proposes a novel deep learning model AR U‐Net++ for segmenting retinal layers and fluids. The four retinal layers ILM (Internal Limiting Membrane), IPL (Inner Plexiform Layer), RPE (Retinal Pigment Epithelium), BM (Bruch Membrane), and IRF (Intra Retinal Fluid), SRF (Sub Retinal Fluid), and PED (Pigment Epithelial Detachment) are segmented using AR U‐Net++. The proposed architecture AR U‐Net++ achieves better accuracy (99.67%), mean IoU (0.84), and dice coefficient (0.94) than the existing models of U‐Net, AR U‐Net, and AR W‐Net. The novelty of the suggested model AR U‐Net++ is to identify the exact location and depth of the retinal fluid in between the retinal layers and generating reports that aids the clinicians in the diagnosis of Age related Macular Degeneration.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Application program interface for automatic segmentation of retinal layers and fluids in Optical Coherence Tomography ‐ Neovascular Age related Macular degeneration retinal images using deep learning models

Jeya Prabha,

Sameera Fathimal,

Meghana

et al. 2023

Int J Imaging Syst Tech

View full text Add to dashboard Cite

show abstract

“…These networks are designed to detect conditions such as Age-Related Macular Degeneration (AMD), DME, and Chronic Neovascularization (CNV) [2] , [3] . Smitha et al [4] proposed an end-to-end model, based on GANs, designed to segment retinal layers from OCT images and subsequently classify those images as either indicating a certain disease or being normal. Rachmadi et al [5] utilized a GAN model to predict the evolution of white matter hyperintensities in small vessel disease.…”

Section: Literature Surveymentioning

confidence: 99%

Generating OCT B-Scan DME images using optimized Generative Adversarial Networks (GANs)

Tripathi¹,

Kumar²,

Mayya³

et al. 2023

Heliyon

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“…He et al [12] utilized GANs to segment retinal layers from OCT B-scan images. Similarly, Smitha and Jidesh [13] proposed an end-to-end model, leveraging GANs, to segment retinal layers from OCT B-scan images, further classifying them as either normal or disease-afflicted.…”

Section: Related Workmentioning

confidence: 99%

Fuzzy Logic-Based System for Identifying the Severity of Diabetic Macular Edema from OCT B-Scan Images Using DRIL, HRF, and Cystoids

et al. 2023

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Diabetic Macular Edema (DME) is a severe ocular complication commonly found in patients with diabetes. The condition can precipitate a significant drop in VA and, in extreme cases, may result in irreversible vision loss. Optical Coherence Tomography (OCT), a technique that yields high-resolution retinal images, is often employed by clinicians to assess the extent of DME in patients. However, the manual interpretation of OCT B-scan images for DME identification and severity grading can be error-prone, with false negatives potentially resulting in serious repercussions. In this paper, we investigate an Artificial Intelligence (AI) driven system that offers an end-to-end automated model, designed to accurately determine DME severity using OCT B-Scan images. This model operates by extracting specific biomarkers such as Disorganization of Retinal Inner Layers (DRIL), Hyper Reflective Foci (HRF), and cystoids from the OCT image, which are then utilized to ascertain DME severity. The rules guiding the fuzzy logic engine are derived from contemporary research in the field of DME and its association with various biomarkers evident in the OCT image. The proposed model demonstrates high efficacy, identifying images with DRIL with 93.3% accuracy and successfully segmenting HRF and cystoids from OCT images with dice similarity coefficients of 91.30% and 95.07% respectively. This study presents a comprehensive system capable of accurately grading DME severity using OCT B-scan images, serving as a potentially invaluable tool in the clinical assessment and treatment of DME.

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Detection of retinal disorders from OCT images using generative adversarial networks

Cited by 9 publications

References 45 publications

Application program interface for automatic segmentation of retinal layers and fluids in Optical Coherence Tomography ‐ Neovascular Age related Macular degeneration retinal images using deep learning models

Application program interface for automatic segmentation of retinal layers and fluids in Optical Coherence Tomography ‐ Neovascular Age related Macular degeneration retinal images using deep learning models

Generating OCT B-Scan DME images using optimized Generative Adversarial Networks (GANs)

Fuzzy Logic-Based System for Identifying the Severity of Diabetic Macular Edema from OCT B-Scan Images Using DRIL, HRF, and Cystoids

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