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Improving Lesion Segmentation for Diabetic Retinopathy Using Adversarial Learning

  • Qiqi Xiao
  • Jiaxu Zou
  • Muqiao Yang
  • Alex Gaudio
  • Kris Kitani
  • Asim SmailagicEmail author
  • Pedro Costa
  • Min Xu
Conference paper
First Online:
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11663)

Abstract

Diabetic Retinopathy (DR) is a leading cause of blindness in working age adults. DR lesions can be challenging to identify in fundus images, and automatic DR detection systems can offer strong clinical value. Of the publicly available labeled datasets for DR, the Indian Diabetic Retinopathy Image Dataset (IDRiD) presents retinal fundus images with pixel-level annotations of four distinct lesions: microaneurysms, hemorrhages, soft exudates and hard exudates. We utilize the HEDNet edge detector to solve a semantic segmentation task on this dataset, and then propose an end-to-end system for pixel-level segmentation of DR lesions by incorporating HEDNet into a Conditional Generative Adversarial Network (cGAN). We design a loss function that adds adversarial loss to segmentation loss. Our experiments show that the addition of the adversarial loss improves the lesion segmentation performance over the baseline.

Keywords

Conditional generative adversarial networks Deep learning Segmentation Medical image analysis 
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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Carnegie Mellon UniversityPittsburghUSA
  2. 2.INESC TECPortoPortugal

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