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Deep-Learning-Based Classification of Rat OCT Images After Intravitreal Injection of ET-1 for Glaucoma Understanding

  • Félix Fuentes-HurtadoEmail author
  • Sandra Morales
  • Jose M. Mossi
  • Valery Naranjo
  • Vadim Fedulov
  • David Woldbye
  • Kristian Klemp
  • Marie Torm
  • Michael Larsen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11314)

Abstract

Optical coherence tomography (OCT) is a useful technique to monitor retinal damage. We present an automatic method to accurately classify rodent OCT images in healthy and pathological (before and after 14 days of intravitreal injection of Endothelin-1, respectively) making use of the DenseNet-201 architecture fine-tuned and a customized top-model. We validated the performance of the method on 1912 OCT images yielding promising results (\(AUC = 0.99\pm 0.01\) in a \(P=15\) leave-P-out cross-validation). Besides, we also compared the results of the fine-tuned network with those achieved training the network from scratch, obtaining some interesting insights. The presented method poses a step forward in understanding pathological rodent OCT retinal images, as at the moment there is no known discriminating characteristic which allows classifying this type of images accurately. The result of this work is a very accurate and robust automatic method to distinguish between healthy and a rodent model of glaucoma, which is the backbone of future works dealing with human OCT images.

Keywords

Optical coherence tomography Deep-learning Glaucoma 

Notes

Acknowledgments

Animal experiment permission was granted by the Danish Animal Experimentation Council (license number: 2017-15-0201-01213). We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research. This work was supported by the Project GALAHAD [H2020-ICT-2016-2017, 732613].

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Félix Fuentes-Hurtado
    • 1
    Email author
  • Sandra Morales
    • 1
  • Jose M. Mossi
    • 1
  • Valery Naranjo
    • 1
  • Vadim Fedulov
    • 2
  • David Woldbye
    • 3
  • Kristian Klemp
    • 3
  • Marie Torm
    • 4
  • Michael Larsen
    • 4
  1. 1.Instituto de Investigación e Innovación en Bioingeniería, I3BUniversitat Politècnica de ValènciaValenciaSpain
  2. 2.Department of Neuroscience and PharmacologyUniversity of CopenhagenCopenhagenDenmark
  3. 3.Laboratory of Neural Plasticity, Department of NeuroscienceUniversity of CopenhagenCopenhagenDenmark
  4. 4.Department of OphthalmologyRigshospitalet-GlostrupGlostrup, CopenhagenDenmark

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