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Iterative Deep Retinal Topology Extraction

  • Carles Ventura
  • Jordi Pont-Tuset
  • Sergi Caelles
  • Kevis-Kokitsi Maninis
  • Luc Van Gool
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11075)

Abstract

This paper tackles the task of estimating the topology of filamentary networks such as retinal vessels. Building on top of a global model that performs a dense semantical classification of the pixels of the image, we design a Convolutional Neural Network (CNN) that predicts the local connectivity between the central pixel of an input patch and its border points. By iterating this local connectivity we sweep the whole image and infer the global topology of the filamentary network, inspired by a human delineating a complex network with the tip of their finger. We perform a qualitative and quantitative evaluation on retinal veins and arteries topology extraction on DRIVE dataset, where we show superior performance to very strong baselines.

Notes

Acknowledgements

This research was supported by the Spanish Ministry of Economy and Competitiveness (TIN2015-66951-C2-2-R grant), by Swiss Commission for Technology and Innovation (CTI, Grant No. 19015.1 PFES-ES, NeGeVA) and by the Universitat Oberta de Catalunya.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Carles Ventura
    • 1
  • Jordi Pont-Tuset
    • 2
  • Sergi Caelles
    • 2
  • Kevis-Kokitsi Maninis
    • 2
  • Luc Van Gool
    • 2
  1. 1.Scene Understading and Artificial Intelligence LabUniversitat Oberta de CatalunyaBarcelonaSpain
  2. 2.Computer Vision Laboratory ETH ZürichZürichSwitzerland

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