Laser Scar Detection in Fundus Images Using Convolutional Neural Networks

  • Qijie Wei
  • Xirong LiEmail author
  • Hao Wang
  • Dayong Ding
  • Weihong Yu
  • Youxin Chen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11364)


In diabetic eye screening programme, a special pathway is designed for those who have received laser photocoagulation treatment. The treatment leaves behind circular or irregular scars in the retina. Laser scar detection in fundus images is thus important for automated DR screening. Despite its importance, the problem is understudied in terms of both datasets and methods. This paper makes the first attempt to detect laser-scar images by deep learning. To that end, we contribute to the community Fundus10K, a large-scale expert-labeled dataset for training and evaluating laser scar detectors. We study in this new context major design choices of state-of-the-art Convolutional Neural Networks including Inception-v3, ResNet and DenseNet. For more effective training we exploit transfer learning that passes on trained weights of ImageNet models to their laser-scar counterparts. Experiments on the new dataset shows that our best model detects laser-scar images with sensitivity of 0.962, specificity of 0.999, precision of 0.974 and AP of 0.988 and AUC of 0.999. The same model is tested on the public LMD-BAPT test set, obtaining sensitivity of 0.765, specificity of 1, precision of 1, AP of 0.975 and AUC of 0.991, outperforming the state-of-the-art with a large margin. Data is available at


Laser scar detection Fundus image Convolutional Neural Network 



This work was supported by the National Natural Science Foundation of China (No. 61672523), the Fundamental Research Funds for the Central Universities and the Research Funds of Renmin University of China (No. 18XNLG19). The authors thank anonymous reviewers for their feedbacks.


  1. 1.
  2. 2.
    Cuadros, J., Bresnick, G.: EyePACS: an adaptable telemedicine system for diabetic retinopathy screening. JDST 3(3), 509–516 (2009)Google Scholar
  3. 3.
    Dias, J., Oliveira, C., da Silva Cruz, L.: Detection of laser marks in retinal images. In: CBMS (2013)Google Scholar
  4. 4.
    Gargeya, R., Leng, T.: Automated identification of diabetic retinopathy using deep learning. Ophthalmology 124(7), 962–969 (2017)CrossRefGoogle Scholar
  5. 5.
    Gulshan, V., et al.: Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 316(22), 2402–2410 (2016)CrossRefGoogle Scholar
  6. 6.
    He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: surpassing human-level performance on ImageNet classification. In: ICCV (2015)Google Scholar
  7. 7.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR (2016)Google Scholar
  8. 8.
    Huang, G., Liu, Z., Weinberger, K., van der Maaten, L.: Densely connected convolutional networks. In: CVPR (2017)Google Scholar
  9. 9.
    Kaggle: Diabetic retinopathy detection (2015).
  10. 10.
    Krizhevsky, A., Sutskever, I., Hinton, G.: ImageNet classification with deep convolutional neural networks. In: NIPS (2012)Google Scholar
  11. 11.
    Li, X., Uricchio, T., Ballan, L., Bertini, M., Snoek, C., Del Bimbo, A.: Socializing the semantic gap: a comparative survey on image tag assignment, refinement and retrieval. ACM Comput. Surv. 49(1), 14:1–14:39 (2016)CrossRefGoogle Scholar
  12. 12.
    Liu, Y., et al.: Prevalence of diabetic retinopathy among 13473 patients with diabetesmellitus in China: a cross-sectional epidemiological survey in sixprovinces. BMJ Open 7(1), e013199 (2017)CrossRefGoogle Scholar
  13. 13.
    Orlando, J., Prokofyeva, E., del Fresno, M., Blaschko, M.: Convolutional neural network transfer for automated glaucoma identification. In: ISMIPA (2017)Google Scholar
  14. 14.
    Pratt, H., Coenen, F., Broadbent, D., Harding, S., Zheng, Y.: Convolutional neural networks for diabetic retinopathy. Procedia Comput. Sci. 90, 200–205 (2016)CrossRefGoogle Scholar
  15. 15.
    Ravishankar, H., et al.: Understanding the mechanisms of deep transfer learning for medical images. In: Carneiro, G., et al. (eds.) LABELS/DLMIA -2016. LNCS, vol. 10008, pp. 188–196. Springer, Cham (2016). Scholar
  16. 16.
    Sousa, J., Oliveira, C., Silva Cruz, L.: Automatic detection of laser marks in retinal digital fundus images. In: EUSIPCO (2016)Google Scholar
  17. 17.
    Syed, A., Akbar, M., Akram, M., Fatima, J.: Automated laser mark segmentation from colored retinal images. In: INMIC (2014)Google Scholar
  18. 18.
    Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: CVPR (2016)Google Scholar
  19. 19.
    Tahir, F., Akram, M., Abbass, M., Khan, A.: Laser marks detection from fundus images. In: HIS (2014)Google Scholar
  20. 20.

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Qijie Wei
    • 1
    • 2
    • 3
  • Xirong Li
    • 1
    • 2
    Email author
  • Hao Wang
    • 1
    • 2
  • Dayong Ding
    • 3
  • Weihong Yu
    • 4
  • Youxin Chen
    • 4
  1. 1.Key Lab of DEKERenmin University of ChinaBeijingChina
  2. 2.AI & Media Computing LabRenmin University of ChinaBeijingChina
  3. 3.Vistel Inc.BeijingChina
  4. 4.Peking Union Medical College HospitalBeijingChina

Personalised recommendations