Patch-Based Deep Convolutional Neural Network for Corneal Ulcer Area Segmentation

  • Qichao Sun
  • Lijie Deng
  • Jianwei Liu
  • Haixiang Huang
  • Jin Yuan
  • Xiaoying TangEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10554)


We present a novel approach to automatically identify the corneal ulcer areas using fluorescein staining images. The proposed method is based on a deep convolutional neural network that labels each pixel in the corneal image as either ulcer area or non-ulcer area, which is essentially a two-class classification problem. Patch-based approach was employed; for every image pixel, a surrounding patch of size 19 × 19 was used to extract the RGB intensities to be used as features for training and testing. For the architecture of our deep network, there were four convolutional layers followed by three fully connected layers with dropout. The final classification was inferred from the probabilistic output from the network. The proposed approach has been validated on a total of 48 images using 5-fold cross-validation, with high segmentation accuracy established; the proposed method was found to be superior to both a baseline method (active contour) and another representative network method (VGG net). Our automated segmentation method had a mean Dice overlap of 0.86 when compared to the manually delineated gold standard as well as a strong and significant manual-vs-automatic correlation in terms of the ulcer area size (correlation coefficient = 0.9934, p-value = 6.3e-45). To the best of our knowledge, this is one of the first few works that have accurately tackled the corneal ulcer area segmentation challenge using deep neural network techniques.


Corneal ulcer Deep learning Convolutional neural network Patch 



This study was supported by the National Key R&D Program of China (2017YFC0112400), the National Natural Science Foundation of China (NSFC 81501546), and the SYSU-CMU Shunde International Joint Research Institute Start-up Grant (20150306).


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Qichao Sun
    • 1
  • Lijie Deng
    • 1
    • 2
  • Jianwei Liu
    • 1
  • Haixiang Huang
    • 3
  • Jin Yuan
    • 3
  • Xiaoying Tang
    • 1
    • 2
    • 4
    Email author
  1. 1.Sun Yat-sen University Carnegie Mellon University (SYSU-CMU) Joint Institute of EngineeringSun Yat-sen UniversityGuangzhouChina
  2. 2.Sun Yat-sen University Carnegie Mellon University (SYSU-CMU) SHUNDE International Joint Research InstituteShundeChina
  3. 3.State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic CentreSun Yat-sen UniversityGuangzhouChina
  4. 4.School of Electronics and Information TechnologySun Yat-sen UniversityGuangzhouChina

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