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Deep feature extraction via adaptive collaborative learning for drusen segmentation from fundus images

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Abstract

Drusen are an early sign of non-neovascular age-related macular degeneration which is a major factor of irreversible blindness. Drusen segmentation plays a vital role in proper diagnosis and prevention of further complications. However, most of the existing drusen segmentation approaches rely on handcrafted features which are not always guaranteed to be discriminative and therefore lead to limited performance. In this paper, we propose a deep feature extraction framework for drusen segmentation. It is formulated as a deep model which can automatically extract discriminative features. Specifically, the framework is mainly composed of three components, including feature learning, loss function and classification. The effectiveness of our method lies in the fact that the deep feature learning procedures are driven by an adaptive collaborative similarity learning technique in loss function. We evaluate the framework on STARE and DRIVE datasets, and the quantitative comparison with the state-of-the-art methods in terms of sensitivity, specificity and accuracy demonstrates the superiority of the proposed method.

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Acknowledgements

This work has been supported by the National Natural Science Foundation of China (61373149, 61672329).

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Authors and Affiliations

  1. School of Information Science and Engineering, Shandong Normal University, Jinan, China

    Xiuxiu Ren, Xiangwei Zheng & Xiao Dong

  2. Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology, Shandong Normal University, Jinan, China

    Xiuxiu Ren & Xiangwei Zheng

  3. School of Information Science and Engineering, Qufu Normal University, Rizhao, China

    Xinchun Cui

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  1. Xiuxiu Ren
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  2. Xiangwei Zheng
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  3. Xiao Dong
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  4. Xinchun Cui
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Correspondence to Xiangwei Zheng.

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Ren, X., Zheng, X., Dong, X. et al. Deep feature extraction via adaptive collaborative learning for drusen segmentation from fundus images. SIViP 15, 895–902 (2021). https://doi.org/10.1007/s11760-020-01812-2

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  • DOI: https://doi.org/10.1007/s11760-020-01812-2

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