Machine learning for glaucoma assessment using fundus images

• Autores: Andrés Yesid Díaz Pinto
• Directores de la Tesis: Valeriana Naranjo Ornedo (dir. tes.), Sandra Morales Martínez (dir. tes.)
• Lectura: En la Universitat Politècnica de València ( España ) en 2019
• Idioma: español
• Tribunal Calificador de la Tesis: Carmen Serrano Gotarredona (presid.), Ignacio Bosch Roig (secret.), Thomas Edgar Walter (voc.)
• Programa de doctorado: Programa de Doctorado en Tecnologías para la Salud y el Bienestar por la Universitat Politècnica de València
• Materias:
  • Matemáticas
    • Ciencia de los ordenadores
      • Inteligencia artificial
  • Ciencias médicas
    • Ciencias clínicas
      • Oftalmología
• Enlaces
  • Tesis en acceso abierto en: RiuNet
• Resumen

  • Fundus images are widely used by ophthalmologists to assess the retina and detect glaucoma, which is, according to studies from the World Health Organization (WHO), the second cause of blindness worldwide.

    In this thesis, machine learning algorithms for automatic glaucoma assessment using fundus images are studied. First, two methods for automatic segmentation are proposed. The first method uses the Stochastic Watershed transformation to segment the optic cup and measures clinical features such as the Cup/Disc ratio and ISNT rule. The second method is a U-Net architecture focused on the optic disc and optic cup segmentation task.

    Secondly, automated glaucoma assessment systems using convolutional neural networks (CNNs) are presented. In this approach, different ImageNet-trained models are fine-tuned and used as automatic glaucoma classifiers. These new techniques allow detecting glaucoma without previous segmentation or feature extraction. Moreover, it improves the performance of other state-of-art works.

    Thirdly, given the difficulty of getting large amounts of glaucoma-labelled images, this thesis addresses the problem of retinal image synthesis. Two different architectures for image synthesis, the Variational Autoencoder (VAE) and Generative Adversarial Networks (GAN) architectures, were analysed. Using these models, synthetic images that were qualitative and quantitative analysed, reporting state-of-the-art performance, were generated.

    Finally, an adversarial model is used to create an alternative automatic glaucoma assessment system. In this part, a semi-supervised learning algorithm was implemented to reach this goal.