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Deep Convolutional Artery/Vein Classification of Retinal Vessels

  • Maria Ines Meyer
  • Adrian Galdran
  • Pedro Costa
  • Ana Maria Mendonça
  • Aurélio Campilho
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10882)

Abstract

The classification of retinal vessels into arteries and veins in eye fundus images is a relevant task for the automatic assessment of vascular changes. This paper presents a new approach to solve this problem by means of a Fully-Connected Convolutional Neural Network that is specifically adapted for artery/vein classification. For this, a loss function that focuses only on pixels belonging to the retinal vessel tree is built. The relevance of providing the model with different chromatic components of the source images is also analyzed. The performance of the proposed method is evaluated on the RITE dataset of retinal images, achieving promising results, with an accuracy of \(96\%\) on large caliber vessels, and an overall accuracy of \(84\%\).

Notes

Acknowledgments

This work is funded by the North Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, and the European Regional Development Fund (ERDF), within the project “NanoSTIMA: Macro-to-Nano Human Sensing: Towards Integrated Multimodal Health Monitoring and Analytics/NORTE-01-0145-FEDER-000016”.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Maria Ines Meyer
    • 1
  • Adrian Galdran
    • 1
  • Pedro Costa
    • 1
  • Ana Maria Mendonça
    • 1
    • 2
  • Aurélio Campilho
    • 1
    • 2
  1. 1.INESC-TEC - Institute for Systems and Computer Engineering, Technology and SciencePortoPortugal
  2. 2.Faculdade de Engenharia da Universidade do PortoPortoPortugal

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