REFUGE Challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs

Orlando, José Ignacio; Fu, Huazhu; Breda, João Barbossa; Keer, Karel Van; Bathula, Deepti R.; Diaz-Pinto, Andrés; Fang, Ruogu; Heng, Pheng‐Ann; Kim, Je Young; Lee, Joon-Ho; Li, Xiaoxiao; Liu, Peng; Lu, Shiping; Murugesan, Balamurali; Naranjo, Valery; Phaye, Sai Samarth R; Shankaranarayana, Sharath M; Sikka, Apoorva; Son, Jaemin; Hengel, Anton van den; Wang, Shujun; Wu, Junyan; Wu, Zifeng; Xu, Guanghui; Xu, Yongli; Yin, Pengshuai; Li, Fēi; Zhang, Xiulan; Xu, Yanwu; Bogunović, Hrvoje

doi:10.1016/j.media.2019.101570

Cited by 499 publications

(288 citation statements)

References 87 publications

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“…The automatic detection of early-stage glaucoma is still one of the most challenging problems in medical image analysis and a number of test frameworks are available for comparison of different methods [23].…”

Section: Discussionmentioning

confidence: 99%

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A new platform designed for glaucoma screening: identifying the risk of glaucomatous optic neuropathy using fundus photography with deep learning architecture together with intraocular pressure measurements

Zaleska-Żmijewska¹,

Szaflik²,

Borowiecki³

et al. 2020

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Section: Discussionmentioning

confidence: 99%

“…Thus, there is a need for development of automatic methods for GON detection based on fundus images. Several reports have proved the efficacy of machine learning in glaucoma [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

A new platform designed for glaucoma screening: identifying the risk of glaucomatous optic neuropathy using fundus photography with deep learning architecture together with intraocular pressure measurements

Zaleska-Żmijewska¹,

Szaflik²,

Borowiecki³

et al. 2020

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“…In the second stage, ROI is fed into the model to train a high-precision segmentation network to obtain the accurate segmentation results of optic disc and cup. The proposed method is trained and evaluated on DRISHTI-GS [17] and REFUGE datasets [18], respectively. The overall flowchart of our proposed method is shown in Figure 1.…”

Section: Methodsmentioning

confidence: 99%

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

Liu

Pan

Song

2020

Preprint

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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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“…In this paper, we conduct experiments on the DRISHTI-GS [17] and REFUGE [18] dataset.The number of dataset is shown in the Table 2.…”

Section: Datasetmentioning

confidence: 99%

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

Liu

Pan

Song

2020

Preprint

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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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REFUGE Challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs

Cited by 499 publications

References 87 publications

A new platform designed for glaucoma screening: identifying the risk of glaucomatous optic neuropathy using fundus photography with deep learning architecture together with intraocular pressure measurements

A new platform designed for glaucoma screening: identifying the risk of glaucomatous optic neuropathy using fundus photography with deep learning architecture together with intraocular pressure measurements

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

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

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