Abstract
Diabetic retinopathy (DR) is a complication of diabetes that severely affects eyes, and can be graded into five levels according to international protocol. However, optimizing a grading model with strong generalization ability requires large balanced training data, which is difficult to collect in general but particularly for the high severity levels. Typical data augmentation methods, including flip and rotation cannot generate data with high diversity. In this paper, we propose a diabetic retinopathy generative adversarial network (DR-GAN) to synthesize high-resolution fundus images, which can be manipulated with arbitrary grading and lesion information. Thus, large-scale generated data can be used for more meaningful augmentation to train a DR grading model. The proposed retina generator is conditioned on vessel and lesion masks, and adaptive grading vectors sampled from the latent grading space, which can be adopted to control the synthesized grading severity. Moreover, multi-scale discriminators are designed to operate from large to small receptive fields, and joint adversarial losses are adopted to optimize the whole network in an end-to-end manner. With extensive experiments evaluated on the EyePACS dataset connected to Kaggle, we validate the effectiveness of our method, which can both synthesize highly realistic (\(1280 \times 1280\)) controllable fundus images and contribute to the DR grading task.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Kaggle diabetic retinopathy detection competition. https://www.kaggle.com/c/diabetic-retinopathy-detection
Costa, P., et al.: Towards adversarial retinal image synthesis. arXiv preprint arXiv:1701.08974 (2017)
Goodfellow, I., et al.: Generative adversarial nets. In: NIPS, pp. 2672–2680 (2014)
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)
Huang, X., Belongie, S.: Arbitrary style transfer in real-time with adaptive instance normalization. In: IEEE ICCV, pp. 1501–1510 (2017)
Karras, T., Laine, S., Aila, T.: A style-based generator architecture for generative adversarial networks. arXiv preprint arXiv:1812.04948 (2018)
Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: IEEE ICCV, pp. 2980–2988 (2017)
Lin, Z., et al.: A framework for identifying diabetic retinopathy based on anti-noise detection and attention-based fusion. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11071, pp. 74–82. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00934-2_9
Niu, Y., et al.: Pathological evidence exploration in deep retinal image diagnosis. arXiv preprint arXiv:1812.02640 (2018)
Porwal, P., et al.: Indian diabetic retinopathy image dataset (IDRID): a database for diabetic retinopathy screening research. Data 3(3), 25 (2018)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Staal, J., Abràmoff, M.D., Niemeijer, M., Viergever, M.A., Van Ginneken, B.: Ridge-based vessel segmentation in color images of the retina. IEEE Transactions on Medical Imaging 23(4), 501–509 (2004)
Wang, T.C., Liu, M.Y., Zhu, J.Y., Tao, A., Kautz, J., Catanzaro, B.: High-resolution image synthesis and semantic manipulation with conditional GANs. In: IEEE CVPR, pp. 8798–8807 (2018)
Wang, Z., Yin, Y., Shi, J., Fang, W., Li, H., Wang, X.: Zoom-in-Net: deep mining lesions for diabetic retinopathy detection. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10435, pp. 267–275. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66179-7_31
Zhao, H., Li, H., Maurer-Stroh, S., Cheng, L.: Synthesizing retinal and neuronal images with generative adversarial nets. Med. Image Anal. 49, 14–26 (2018)
Zhou, Y., et al.: Collaborative learning of semi-supervised segmentation and classification for medical images. In: IEEE CVPR (2019)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
1 Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Zhou, Y., He, X., Cui, S., Zhu, F., Liu, L., Shao, L. (2019). High-Resolution Diabetic Retinopathy Image Synthesis Manipulated by Grading and Lesions. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11764. Springer, Cham. https://doi.org/10.1007/978-3-030-32239-7_56
Download citation
DOI: https://doi.org/10.1007/978-3-030-32239-7_56
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-32238-0
Online ISBN: 978-3-030-32239-7
eBook Packages: Computer ScienceComputer Science (R0)
