Representation Learning for Retinal Vasculature Embeddings

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10554)

Abstract

The retinal vasculature imaged with fundus photography has the potential of encoding precious information for image-based retinal biomarkers, however, progress in their development is slow due to the need of defining vasculature morphology variables a priori and developing algorithms specific to these variables. In this paper, we introduce a novel approach to learn a general descriptor (or embedding) that captures the vasculature morphology in a numerically compact vector with minimal feature engineering. The vasculature embedding is computed by leveraging the internal representation of a new encoder-enhanced fully convolutional neural network, trained end-to-end with the raw pixels and manually segmented vessels. This approach effectively transfers the vasculature patterns learned by the network into a general purpose vasculature embedding vector. Using Messidor and Messidor-2, two publicly available datasets, we test the vasculature embeddings on two tasks: (1) an image retrieval task, which retrieved similar images according to their vasculature; (2) a diabetic retinopathy classification task, where we show how the vasculature embeddings improve the classification of an algorithm based on microaneurysms detection by 0.04 AUC on average.

Notes

Acknowledgement

This work has been supported by the Center for Precision Health and School of Biomedical Informatics at University of Texas Health Science Center at Houston. We would like to thank Daniele Cortinovis for the initial implementation of U-Net on https://github.com/orobix/retina-unet. The Messidor and Messidor-2 datasets are kindly provided by the LaTIM laboratory (see http://latim.univ-brest.fr/) and the Messidor program partners (see http://messidor.crihan.fr/)”.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Luca Giancardo
    • 1
    • 2
  • Kirk Roberts
    • 1
  • Zhongming Zhao
    • 1
    • 2
  1. 1.School of Biomedical InformaticsUniversity of Texas Health Science Center at Houston (UTHealth)HoustonUSA
  2. 2.Center for Precision HealthUTHealthHoustonUSA

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