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

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/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

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

10.21203/rs.3.rs-56881/v1 ◽

2020 ◽

Author(s):

Bingyan Liu ◽

Daru Pan ◽

Hui Song

Keyword(s):

Optic Disc ◽

Vision Loss ◽

Semantic Segmentation ◽

Post Process ◽

Main Indicator ◽

Segmentation Methods ◽

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 is the main indicator used to screen and diagnose glaucoma, optic disc and optic cup segmentation can assist computer in diagnosing glaucoma.Therefore, accurate segmentation of optic disc and optic cup is beneficial to the screening and diagnosis of glaucoma and helps patients diagnose and treat early. Method: In this paper, we consider the segmentation of the optic disc and the optic cup as a multi-category semantic segmentation task, and proposed a deep learning-based model model named DDSC-Net(densely connected depthwise separable convolution network) to extract the optic disc and the optic cup. The backbone network is composed of densely connected deep separable convolution blocks to form a deeper network and the image pyramid input is introduce into the input layer to widen the network. In the post-process, we apply the morphological method to refine the output segmentation results. Result: The proposed method is evaluated on two publicly available datasets, DRISHTI-GS dataset and REFUGE dataset. And the experiment results show that our DDSC-Net outperforms state-of-the-art optic disc and cup segmentation methods in terms of disc coefficients and Jaccard score. Furthermore,our method achieves the best result on the more challenging optic cup segmentation task. Conclusion: The promising segmention performances reveal that our method has potential in assisting the screening and diagnosis of the glaucoma.

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

10.21203/rs.3.rs-54643/v1 ◽

2020 ◽

Author(s):

Bingyan Liu ◽

Daru Pan ◽

Hui Song

Keyword(s):

Optic Disc ◽

Vision Loss ◽

Semantic Segmentation ◽

Post Process ◽

Main Indicator ◽

Segmentation Methods ◽

Optic Cup ◽

Segmentation Task ◽

Screening And Diagnosis ◽

Cup To Disc Ratio

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Improved OD and OC Fragmentation using Deep Learning for the Progression Recognition of Glaucoma

10.21203/rs.3.rs-490801/v1 ◽

2021 ◽

Author(s):

Shanmugam P ◽

Raja J ◽

Pitchai R

Keyword(s):

Deep Learning ◽

General Population ◽

Optic Disc ◽

State Of The Art ◽

Initial Stage ◽

Optic Cup ◽

Overall Performance ◽

Cup To Disc Ratio

Abstract Glaucoma is one of the most hazardous diseases, proceeding to impact and burden an extensive bit of our general population. Appropriately, the initial stage of identification of glaucoma is significant to prevent the permanent vision misfortune. The CDR is the important factor for glaucoma recognition. The precise fragmentation of optic disc and cup is yet an evolving issue. Most of the segmentation based glaucoma recognition methods depends on the handcrafted features. It affects the overall performance of the glaucoma recognition. To resolve this issue, an efficient deep learning based optic cup and disc segmentation using technique multi-label segmentation Au-net has been developed in this paper. The proposed method focusing on the optic cup-to-disc ratio for the recognition of glaucoma, which may be the best system for building a capable, energetic, and accurate structure for glaucoma analysis. This system has been simulated on DRISHTI datasets. The exploratory outcomes indicates the proposed strategy performs well to the best with state-of-the-art methodologies accomplishing a 99% of Accuracy, 88% of Sensitivity and 95.5% of Specificity on the DRISHTI GS1 dataset individually.

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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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Attention-Based Multi-Context Guiding for Few-Shot Semantic Segmentation

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v33i01.33018441 ◽

2019 ◽

Vol 33 ◽

pp. 8441-8448 ◽

Cited By ~ 14

Author(s):

Tao Hu ◽

Pengwan Yang ◽

Chiliang Zhang ◽

Gang Yu ◽

Yadong Mu ◽

...

Keyword(s):

Deep Learning ◽

Feature Fusion ◽

State Of The Art ◽

Semantic Segmentation ◽

Research Topic ◽

Context Information ◽

Multi Scale ◽

Support Set ◽

Segmentation Task ◽

Context Features

Few-shot learning is a nascent research topic, motivated by the fact that traditional deep learning methods require tremendous amounts of data. The scarcity of annotated data becomes even more challenging in semantic segmentation since pixellevel annotation in segmentation task is more labor-intensive to acquire. To tackle this issue, we propose an Attentionbased Multi-Context Guiding (A-MCG) network, which consists of three branches: the support branch, the query branch, the feature fusion branch. A key differentiator of A-MCG is the integration of multi-scale context features between support and query branches, enforcing a better guidance from the support set. In addition, we also adopt a spatial attention along the fusion branch to highlight context information from several scales, enhancing self-supervision in one-shot learning. To address the fusion problem in multi-shot learning, Conv-LSTM is adopted to collaboratively integrate the sequential support features to elevate the final accuracy. Our architecture obtains state-of-the-art on unseen classes in a variant of PASCAL VOC12 dataset and performs favorably against previous work with large gains of 1.1%, 1.4% measured in mIoU in the 1-shot and 5-shot setting.

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Improved optic disc and cup segmentation in Glaucomatic images using deep learning architecture

Multimedia Tools and Applications ◽

10.1007/s11042-020-10430-6 ◽

2021 ◽

Author(s):

Partha Sarathi Mangipudi ◽

Hari Mohan Pandey ◽

Ankur Choudhary

Keyword(s):

Deep Learning ◽

Optic Disc ◽

Vision Loss ◽

State Of The Art ◽

Key Factors ◽

Glaucoma Diagnosis ◽

Proposed Model ◽

Effective System ◽

Benchmark Datasets ◽

Multiple Experts

AbstractGlaucoma is an ailment causing permanent vision loss but can be prevented through the early detection. Optic disc to cup ratio is one of the key factors for glaucoma diagnosis. But accurate segmentation of disc and cup is still a challenge. To mitigate this challenge, an effective system for optic disc and cup segmentation using deep learning architecture is presented in this paper. Modified Groundtruth is utilized to train the proposed model. It works as fused segmentation marking by multiple experts that helps in improving the performance of the system. Extensive computer simulations are conducted to test the efficiency of the proposed system. For the implementation three standard benchmark datasets such as DRISHTI-GS, DRIONS-DB and RIM-ONE v3 are used. The performance of the proposed system is validated against the state-of-the-art methods. Results indicate an average overlapping score of 96.62%, 96.15% and 98.42% respectively for optic disc segmentation and an average overlapping score of 94.41% is achieved on DRISHTI-GS which is significant for optic cup segmentation.

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

Sensors ◽

10.3390/s22020434 ◽

2022 ◽

Vol 22 (2) ◽

pp. 434

Author(s):

Marriam Nawaz ◽

Tahira Nazir ◽

Ali Javed ◽

Usman Tariq ◽

Hwan-Seung Yong ◽

...

Keyword(s):

Deep Learning ◽

Optic Disc ◽

Vision Loss ◽

Image Database ◽

Eye Color ◽

Glaucoma Screening ◽

Glaucoma Detection ◽

Retinal Fundus Image ◽

Optic Cup ◽

Screening Procedures

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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