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Patch-Based Generative Adversarial Network Towards Retinal Vessel Segmentation

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Neural Information Processing (ICONIP 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1142))

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Abstract

Retinal blood vessels are considered to be the reliable diagnostic biomarkers of ophthalmologic and diabetic retinopathy. Monitoring and diagnosis totally depends on expert analysis of both thin and thick retinal vessels which has recently been carried out by various artificial intelligent techniques. Existing deep learning methods attempt to segment retinal vessels using a unified loss function optimized for both thin and thick vessels with equal importance. Due to variable thickness, biased distribution, and difference in spatial features of thin and thick vessels, unified loss function are more influential towards identification of thick vessels resulting in weak segmentation. To address this problem, a conditional patch-based generative adversarial network is proposed which utilizes a generator network and a patch-based discriminator network conditioned on the sample data with an additional loss function to learn both thin and thick vessels. Experiments are conducted on publicly available STARE and DRIVE datasets which show that the proposed model outperforms the state-of-the-art methods.

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Authors and Affiliations

  1. Cloud Application Solutions Division, Mentor, A Siemens Business, Lahore, Pakistan

    Waseem Abbas

  2. Department of Computer Science, Syed Babar Ali School of Science and Engineering, Lahore University of Management Sciences (LUMS), Lahore, Pakistan

    Muhammad Haroon Shakeel, Numan Khurshid & Murtaza Taj

Authors
  1. Waseem Abbas
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  2. Muhammad Haroon Shakeel
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  3. Numan Khurshid
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  4. Murtaza Taj
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Corresponding author

Correspondence to Muhammad Haroon Shakeel .

Editor information

Editors and Affiliations

  1. Australian National University, Canberra, ACT, Australia

    Tom Gedeon

  2. Murdoch University, Murdoch, WA, Australia

    Kok Wai Wong

  3. Kyungpook National University, Daegu, Korea (Republic of)

    Minho Lee

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Abbas, W., Shakeel, M.H., Khurshid, N., Taj, M. (2019). Patch-Based Generative Adversarial Network Towards Retinal Vessel Segmentation. In: Gedeon, T., Wong, K., Lee, M. (eds) Neural Information Processing. ICONIP 2019. Communications in Computer and Information Science, vol 1142. Springer, Cham. https://doi.org/10.1007/978-3-030-36808-1_6

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  • DOI: https://doi.org/10.1007/978-3-030-36808-1_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-36807-4

  • Online ISBN: 978-3-030-36808-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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