scholarly journals A coarse-to-fine deep learning framework for optic disc segmentation in fundus images

2019 ◽  
Vol 51 ◽  
pp. 82-89 ◽  
Author(s):  
Lei Wang ◽  
Han Liu ◽  
Yaling Lu ◽  
Hang Chen ◽  
Jian Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Optic Disc ◽  
Fundus Images ◽  
Learning Framework ◽  
Coarse To Fine

scholarly journals Automated segmentation of the optic disc from fundus images using an asymmetric deep learning network

2021 ◽  
pp. 107810
Author(s):  
Lei Wang ◽  
Juan Gu ◽  
Yize Chen ◽  
Yuanbo Liang ◽  
Weijie Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Optic Disc ◽  
Automated Segmentation ◽  
Fundus Images ◽  
Learning Network ◽  
Deep Learning Network

scholarly journals Segmenting the Optic Disc Using a Deep Learning Ensemble Model Based on OWA Operators

2021 ◽  
Author(s):  
Mohammed Yousef Salem Ali ◽  
Mohamed Abdel-Nasser ◽  
Mohammed Jabreel ◽  
Aida Valls ◽  
Marc Baget
Keyword(s):  
Deep Learning ◽  
Optic Disc ◽  
Vision Loss ◽  
Semantic Segmentation ◽  
Medical Image Segmentation ◽  
Weighted Averaging ◽  
Fundus Images ◽  
Owa Operators ◽  
Detection Systems ◽  
Glaucoma Detection

The optic disc (OD) is the point where the retinal vessels begin. OD carries essential information linked to Diabetic Retinopathy and glaucoma that may cause vision loss. Therefore, accurate segmentation of the optic disc from eye fundus images is essential to develop efficient automated DR and glaucoma detection systems. This paper presents a deep learning-based system for OD segmentation based on an ensemble of efficient semantic segmentation models for medical image segmentation. The aggregation of the different DL models was performed with the ordered weighted averaging (OWA) operators. We proposed the use of a dynamically generated set of weights that can give a different contribution to the models according to their performance during the segmentation of OD in the eye fundus images. The effectiveness of the proposed system was assessed on a fundus image dataset collected from the Hospital Sant Joan de Reus. We obtained Jaccard, Dice, Precision, and Recall scores of 95.40, 95.10, 96.70, and 93.90%, respectively.

scholarly journals Correction to: Two-stage framework for optic disc localization and glaucoma classification in retinal fundus images using deep learning

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Muhammad Naseer Bajwa ◽  
Muhammad Imran Malik ◽  
Shoaib Ahmed Siddiqui ◽  
Andreas Dengel ◽  
Faisal Shafait ◽  
...  
Keyword(s):  
Deep Learning ◽  
Optic Disc ◽  
Fundus Images ◽  
Two Stage ◽  
Optic Disc Localization ◽  
Retinal Fundus Images ◽  
Retinal Fundus

scholarly journals Medinoid: Computer-Aided Diagnosis and Localization of Glaucoma Using Deep Learning †

2019 ◽  
Vol 9 (15) ◽  
pp. 3064 ◽  
Author(s):  
Mijung Kim ◽  
Jong Chul Han ◽  
Seung Hyup Hyun ◽  
Olivier Janssens ◽  
Sofie Van Hoecke ◽  
...  
Keyword(s):  
Deep Learning ◽  
Predictive Model ◽  
Optic Disc ◽  
Medical Images ◽  
Computer Aided Diagnosis ◽  
Fundus Images ◽  
Glaucoma Diagnosis ◽  
Computer Aided ◽  
Different Types ◽  
Aided Diagnosis

Glaucoma is a leading eye disease, causing vision loss by gradually affecting peripheral vision if left untreated. Current diagnosis of glaucoma is performed by ophthalmologists, human experts who typically need to analyze different types of medical images generated by different types of medical equipment: fundus, Retinal Nerve Fiber Layer (RNFL), Optical Coherence Tomography (OCT) disc, OCT macula, perimetry, and/or perimetry deviation. Capturing and analyzing these medical images is labor intensive and time consuming. In this paper, we present a novel approach for glaucoma diagnosis and localization, only relying on fundus images that are analyzed by making use of state-of-the-art deep learning techniques. Specifically, our approach towards glaucoma diagnosis and localization leverages Convolutional Neural Networks (CNNs) and Gradient-weighted Class Activation Mapping (Grad-CAM), respectively. We built and evaluated different predictive models using a large set of fundus images, collected and labeled by ophthalmologists at Samsung Medical Center (SMC). Our experimental results demonstrate that our most effective predictive model is able to achieve a high diagnosis accuracy of 96%, as well as a high sensitivity of 96% and a high specificity of 100% for Dataset-Optic Disc (OD), a set of center-cropped fundus images highlighting the optic disc. Furthermore, we present Medinoid, a publicly-available prototype web application for computer-aided diagnosis and localization of glaucoma, integrating our most effective predictive model in its back-end.

scholarly journals Deep learning based approach for optic disc and optic cup semantic segmentation for glaucoma analysis in retinal fundus images

2020 ◽  
Vol 11 (2) ◽  
pp. 111-120
Author(s):  
Dunja Božić-Štulić ◽  
Maja Braović ◽  
Darko Stipaničev
Keyword(s):  
Deep Learning ◽  
Optic Disc ◽  
Contextual Information ◽  
Automatic Detection ◽  
Semantic Segmentation ◽  
Fundus Images ◽  
Glaucoma Detection ◽  
Average Accuracy ◽  
Optic Cup ◽  
Retinal Fundus

Optic disc and optic cup are one of the most recognized retinal landmarks, and there are numerous methods for their automatic detection. Segmented optic disc and optic cup are useful in providing the contextual information about the retinal image that can aid in the detection of other retinal features, but it is also useful in the automatic detection and monitoring of glaucoma. This paper proposes a deep learning based approach for the automatic optic disc and optic cup semantic segmentation, but also the new model for possible glaucoma detection. The proposed method was trained on DRIVE and DIARETDB1 image datasets and evaluated on MESSIDOR dataset, where it achieved the average accuracy of 97.3% of optic disc and 88.1% of optic cup. Detection rate of glaucoma diesis is 96.75%

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