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3D augmented fundus images for identifying glaucoma via transferred convolutional neural networks

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Abstract

Purpose

Glaucoma is a chronic and irreversible retinopathy threatening the vision of millions of patients around the world. Its early diagnosis and treatment can help to prolong the period of sight deterioration from no visual impairment to blindness, whereas the screening and diagnosis of glaucoma in clinical remains challenging because some key assessment criteria like cup-to-disc ratio is limited by subjective analysis and intra- and inter-observer variability. This paper exploits the potential of new augmented image data of the optic nerve head (ONH) combining with the latest deep learning networks to achieve better diagnosis of glaucoma.

Methods

This paper explores the potential value of additional three-dimensional topographic map of the optic nerve head proceeded by the latest deep learning approaches, i.e. convolutional neural networks to improve the diagnosis efficiency. Specifically, 3D topography map of the ONH and RGB fundus image has been used to train the transferred AlexNet and VGG-16 networks. The diagnostic performance is compared to those achieved by using the 2D fundus images only.

Results

The 3D topographic map of ONH reconstructed from the shape from shading method provides better visualization of the structure of optic cup and disc. These new enhanced dataset was employed to train the proposed deep learning networks and finally achieve diagnostic accuracy of 94.3% which is superior to the networks trained via 2D conventional images.

Conclusion

Employing the deep learning neural networks with augmented 3D images can increase the accuracy of automatic separating glaucoma and non-glaucoma fundus images. It may be used as an objective tool in developing computer assisted diagnosis systems for assessment of glaucoma.

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Data availability

The authors confirm that the data supporting the findings of this study are available within the articles [22,23,24,25] and their supplementary materials.

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Funding

This study was funded by Shanghai University of Medicine and Health Sciences (Innovative and Collaborative Project Funding of Shanghai University of Medicine and Health Sciences under grant number SPCI-17–18-001).

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Authors and Affiliations

  1. Department of Radiology, Shanghai University of Medicine and Health Sciences Affliated Zhoupu Hospital, Shanghai, 201318, China

    Peipei Wang & Mingyuan Yuan

  2. Department of Ophthalmology, The Second Xiangya Hospital, Central South University, Changsha, 410011, Hunan Province, China

    Yan He

  3. Hunan Clinical Research Center of Ophthalmic Disease, Changsha, 410011, Hunan, China

    Yan He

  4. Clinical Immunology Center, Central South University, Changsha, 410011, Hunan Province, China

    Yan He

  5. College of Medical Imaging, Shanghai University of Medicine & Health Sciences, Shanghai, 201318, China

    Peipei Wang & Jiuai Sun

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  1. Peipei Wang
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  2. Mingyuan Yuan
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Contributions

P.P. Wang carried out the experiment and drafted the manuscript with support from all authors. J. Sun conceived the original project and M.Y. Yuan helped to supervise the project.

Corresponding author

Correspondence to Jiuai Sun.

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The authors declare that they have no competing interests.

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Wang, P., Yuan, M., He, Y. et al. 3D augmented fundus images for identifying glaucoma via transferred convolutional neural networks. Int Ophthalmol 41, 2065–2072 (2021). https://doi.org/10.1007/s10792-021-01762-9

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  • DOI: https://doi.org/10.1007/s10792-021-01762-9

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