Deep-Learning-Based Classification of Rat OCT Images After Intravitreal Injection of ET-1 for Glaucoma Understanding
Optical coherence tomography (OCT) is a useful technique to monitor retinal damage. We present an automatic method to accurately classify rodent OCT images in healthy and pathological (before and after 14 days of intravitreal injection of Endothelin-1, respectively) making use of the DenseNet-201 architecture fine-tuned and a customized top-model. We validated the performance of the method on 1912 OCT images yielding promising results (\(AUC = 0.99\pm 0.01\) in a \(P=15\) leave-P-out cross-validation). Besides, we also compared the results of the fine-tuned network with those achieved training the network from scratch, obtaining some interesting insights. The presented method poses a step forward in understanding pathological rodent OCT retinal images, as at the moment there is no known discriminating characteristic which allows classifying this type of images accurately. The result of this work is a very accurate and robust automatic method to distinguish between healthy and a rodent model of glaucoma, which is the backbone of future works dealing with human OCT images.
KeywordsOptical coherence tomography Deep-learning Glaucoma
Animal experiment permission was granted by the Danish Animal Experimentation Council (license number: 2017-15-0201-01213). We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research. This work was supported by the Project GALAHAD [H2020-ICT-2016-2017, 732613].
- 2.Pekala, M., Joshi, N., Freund, D.E., Bressler, N.M., et al.: Deep learning based retinal OCT segmentation. arXiv preprint arXiv:1801.09749 (2018)
- 4.Lee, C.S., Baughman, D.M., Lee, A.Y.: Deep learning is effective for the classification of OCT images of normal versus age-related macular degeneration. arXiv preprint arXiv:1612.04891 (2016)
- 5.Muhammad, H., Fuchs, T.J., De Cuir, N., De Moraes, C.G., et al.: Hybrid deep learning on single wide-field optical coherence tomography scans accurately classifies glaucoma suspects. J. Glaucoma 26(12), 1086–1094 (2017)Google Scholar
- 9.Huang, G., Liu, Z., Weinberger, K.Q., van der Maaten, L.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, p. 3 (2017)Google Scholar
- 10.Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)