An Efficient Deep Learning Approach to Automatic Glaucoma Detection Using Optic Disc and Optic Cup Localization

Glaucoma is an eye disease initiated due to excessive intraocular pressure inside it and caused complete sightlessness at its progressed stage. Whereas timely glaucoma screening-based treatment can save the patient from complete vision loss. Accurate screening procedures are dependent on the availability of human experts who performs the manual analysis of retinal samples to identify the glaucomatous-affected regions. However, due to complex glaucoma screening procedures and shortage of human resources, we often face delays which can increase the vision loss ratio around the globe. To cope with the challenges of manual systems, there is an urgent demand for designing an effective automated framework that can accurately identify the Optic Disc (OD) and Optic Cup (OC) lesions at the earliest stage. Efficient and effective identification and classification of glaucomatous regions is a complicated job due to the wide variations in the mass, shade, orientation, and shapes of lesions. Furthermore, the extensive similarity between the lesion and eye color further complicates the classification process. To overcome the aforementioned challenges, we have presented a Deep Learning (DL)-based approach namely EfficientDet-D0 with EfficientNet-B0 as the backbone. The presented framework comprises three steps for glaucoma localization and classification. Initially, the deep features from the suspected samples are computed with the EfficientNet-B0 feature extractor. Then, the Bi-directional Feature Pyramid Network (BiFPN) module of EfficientDet-D0 takes the computed features from the EfficientNet-B0 and performs the top-down and bottom-up keypoints fusion several times. In the last step, the resultant localized area containing glaucoma lesion with associated class is predicted. We have confirmed the robustness of our work by evaluating it on a challenging dataset namely an online retinal fundus image database for glaucoma analysis (ORIGA). Furthermore, we have performed cross-dataset validation on the High-Resolution Fundus (HRF), and Retinal Image database for Optic Nerve Evaluation (RIM ONE DL) datasets to show the generalization ability of our work. Both the numeric and visual evaluations confirm that EfficientDet-D0 outperforms the newest frameworks and is more proficient in glaucoma classification.

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

10.3233/faia210149 ◽

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.

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

International journal of electrical and computer engineering systems ◽

10.32985/ijeces.11.2.6 ◽

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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Joint Optic Disc and Cup Segmentation Based on Densely Connected Depthwise Separable Convolution Deep Network

10.21203/rs.3.rs-56881/v3 ◽

2020 ◽

Author(s):

Bingyan Liu ◽

Daru Pan ◽

Hui Song

Keyword(s):

Deep Learning ◽

Optic Disc ◽

Vision Loss ◽

State Of The Art ◽

Semantic Segmentation ◽

Important Indicator ◽

Optic Cup ◽

Segmentation Task ◽

Screening And Diagnosis ◽

Cup To Disc Ratio

Abstract Background: Glaucoma is an eye disease that causes vision loss and even blindness. The cup to disc ratio (CDR) is an important indicator for glaucoma screening and diagnosis. Accurate segmentation for the optic disc and cup helps obtain CDR. Although many deep learning-based methods have been proposed to segment the disc and cup for fundus image, achieving highly accurate segmentation performance is still a great challenge due to the heavy overlap between the optic disc and cup.Methods: In this paper, we propose a two-stage method where the optic disc is firstly located and then the optic disc and cup are segmented jointly according to the interesting areas. Also, we consider the joint optic disc and cup segmentation task as a multi-category semantic segmentation task for which a deep learning-based model named DDSC-Net (densely connected depthwise separable convolution network) is proposed. Specifically, we employ depthwise separable convolutional layer and image pyramid input to form a deeper and wider networkto improve segmentation performance. Finally, we evaluate our method on two publicly available datasets, Drishti-GS and REFUGE dataset.Results: The experiment results show that the proposed method outperforms state-of-the-art methods, such as pOSAL, GL-Net, M-Net and Stack-U-Net in terms of disc coefficients, with the scores of 0.9780 (optic disc) and 0.9123 (optic cup) on the DRISHTI-GS dataset, and the scores of 0.9601 (optic disc) and 0.8903 (optic cup) on the REFUGE dataset. Particularly, in the more challenging optic cup segmentation task, our method outperforms GL-Net by 0.7 % in terms of disc coefficients on the Drishti-GS dataset and outperforms pOSAL by 0.79 %on the REFUGE dataset, respectively.Conclusions: The promising segmentation performances reveal that our method has the potential in assisting the screening and diagnosis of glaucoma.

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Joint optic disc and cup segmentation based on densely connected depthwise separable convolution deep network

BMC Medical Imaging ◽

10.1186/s12880-020-00528-6 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Bingyan Liu ◽

Daru Pan ◽

Hui Song

Keyword(s):

Deep Learning ◽

Optic Disc ◽

Vision Loss ◽

State Of The Art ◽

Semantic Segmentation ◽

Important Indicator ◽

Optic Cup ◽

Segmentation Task ◽

Screening And Diagnosis ◽

Cup To Disc Ratio

Abstract Background Glaucoma is an eye disease that causes vision loss and even blindness. The cup to disc ratio (CDR) is an important indicator for glaucoma screening and diagnosis. Accurate segmentation for the optic disc and cup helps obtain CDR. Although many deep learning-based methods have been proposed to segment the disc and cup for fundus image, achieving highly accurate segmentation performance is still a great challenge due to the heavy overlap between the optic disc and cup. Methods In this paper, we propose a two-stage method where the optic disc is firstly located and then the optic disc and cup are segmented jointly according to the interesting areas. Also, we consider the joint optic disc and cup segmentation task as a multi-category semantic segmentation task for which a deep learning-based model named DDSC-Net (densely connected depthwise separable convolution network) is proposed. Specifically, we employ depthwise separable convolutional layer and image pyramid input to form a deeper and wider network to improve segmentation performance. Finally, we evaluate our method on two publicly available datasets, Drishti-GS and REFUGE dataset. Results The experiment results show that the proposed method outperforms state-of-the-art methods, such as pOSAL, GL-Net, M-Net and Stack-U-Net in terms of disc coefficients, with the scores of 0.9780 (optic disc) and 0.9123 (optic cup) on the DRISHTI-GS dataset, and the scores of 0.9601 (optic disc) and 0.8903 (optic cup) on the REFUGE dataset. Particularly, in the more challenging optic cup segmentation task, our method outperforms GL-Net by 0.7$$\%$$ % in terms of disc coefficients on the Drishti-GS dataset and outperforms pOSAL by 0.79$$\%$$ % on the REFUGE dataset, respectively. Conclusions The promising segmentation performances reveal that our method has the potential in assisting the screening and diagnosis of glaucoma.

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Joint Optic Disc and Cup Segmentation Based on Densely Connected Depthwise Separable Convolution Deep Network

10.21203/rs.3.rs-56881/v2 ◽

2020 ◽

Author(s):

Bingyan Liu ◽

Daru Pan ◽

Hui Song

Keyword(s):

Deep Learning ◽

Optic Disc ◽

Vision Loss ◽

State Of The Art ◽

Semantic Segmentation ◽

Important Indicator ◽

Optic Cup ◽

Segmentation Task ◽

Screening And Diagnosis ◽

Cup To Disc Ratio

Abstract Background: Glaucoma is an eye disease that causes vision loss and even blindness. The cup to disc ratio (CDR) is an important indicator for glaucoma screening and diagnosis. Accurate segmentation for the optic disc and cup helps obtain CDR. Although many deep learning-based methods have been proposed to segment the disc and cup for fundus image, achieving highly accurate segmentation performance is still a great challenge due to the heavy overlap between the optic disc and cup. Methods: In this paper, we propose a two-stage method where the optic disc is firstly located and then the optic disc and cup are segmented jointly according to the interesting areas. Also, we consider the joint optic disc and cup segmentation task as a multi-category semantic segmentation task for which a deep learning-based model named DDSC-Net (densely connected depthwise separable convolution network) is proposed. Specifically, we employ depthwise separable convolutional layer and image pyramid input to form a deeper and wider network to improve segmentation performance. Finally, we evaluate our method on two publicly available datasets, Drishti-GS and REFUGE dataset. Results: The experiment results show that the proposed method outperforms state-of-the-art methods, such as pOSAL, GL-Net, M-Net and Stack-U-Net in terms of disc coefficients, with the scores of 0.9780 (optic disc) and 0.9123 (optic cup) on the DRISHTI-GS dataset, and the scores of 0.9601 (optic disc) and 0.8903 (optic cup) on the REFUGE dataset. Particularly, in the more challenging optic cup segmentation task, our method outperforms GL-Net by 0.7 % in terms of disc coefficients on the Drishti-GS dataset and outperforms pOSAL by 0.79 % on the REFUGE dataset, respectively. Conclusions: The promising segmentation performances reveal that our method has the potential in assisting the screening and diagnosis of glaucoma.

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Vision Atrophy Screening and Revelation

10.3233/apc210098 ◽

2021 ◽

Author(s):

Kiruthiga Devi M ◽

Lingamuthu K ◽

Baskar M ◽

Deepa B ◽

Merlin G

Keyword(s):

Optic Disc ◽

Vision Loss ◽

Low Cost ◽

Remote Diagnostics ◽

Glaucoma Detection ◽

Screening Services ◽

Detection And Diagnosis ◽

Retinal Fundus ◽

To Receive

Glaucoma which is known as the “thief of sight”, is an irreversible eye disease It is mainly caused by increased intraocular pressure (IOP), or loss of blood supply to the optic nerve. Glaucoma detection and diagnosis is very important. By analyzing the optic disc and its surroundings, This paper introduces a method for providing automated glaucoma screening services based on a framework that proposes a retinal image synthesizer for glaucoma assessment by analyzing the optic disc and its surroundings. The Cup to Disc Ratio (CDR) is critical for the system, and it is calculated using 2-D retinal fundus images. The synthetic images produced by our system are compared quantitatively. The structural properties of synthetic and real images are analyzed, and the quality of colour is calculated by extracting the 2-D histogram. The system allows patients to receive low-cost remote diagnostics from a distance, preventing blindness and vision loss by early detection and management.

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Mixed Maximum Loss Design for Optic Disc and Optic Cup Segmentation with Deep Learning from Imbalanced Samples

Sensors ◽

10.3390/s19204401 ◽

2019 ◽

Vol 19 (20) ◽

pp. 4401 ◽

Cited By ~ 3

Author(s):

Yong-li Xu ◽

Shuai Lu ◽

Han-xiong Li ◽

Rui-rui Li

Keyword(s):

Deep Learning ◽

Optic Disc ◽

Learning Strategy ◽

State Of The Art ◽

Loss Minimization ◽

Training Strategy ◽

Multi Scale ◽

Maximum Loss ◽

Screening Performance ◽

Optic Cup

Glaucoma is a serious eye disease that can cause permanent blindness and is difficult to diagnose early. Optic disc (OD) and optic cup (OC) play a pivotal role in the screening of glaucoma. Therefore, accurate segmentation of OD and OC from fundus images is a key task in the automatic screening of glaucoma. In this paper, we designed a U-shaped convolutional neural network with multi-scale input and multi-kernel modules (MSMKU) for OD and OC segmentation. Such a design gives MSMKU a rich receptive field and is able to effectively represent multi-scale features. In addition, we designed a mixed maximum loss minimization learning strategy (MMLM) for training the proposed MSMKU. This training strategy can adaptively sort the samples by the loss function and re-weight the samples through data enhancement, thereby synchronously improving the prediction performance of all samples. Experiments show that the proposed method has obtained a state-of-the-art breakthrough result for OD and OC segmentation on the RIM-ONE-V3 and DRISHTI-GS datasets. At the same time, the proposed method achieved satisfactory glaucoma screening performance on the RIM-ONE-V3 and DRISHTI-GS datasets. On datasets with an imbalanced distribution between typical and rare sample images, the proposed method obtained a higher accuracy than existing deep learning methods.

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