Abstract
Optical coherence tomography (OCT) is used to diagnose and track progression of age-related macular degeneration (AMD). Drusen, which appear as bumps between Bruch’s membrane (BM) and the retinal pigment epithelium (RPE) layer, are among the most important biomarkers for staging AMD. In this work, we develop and compare three automated methods for Drusen segmentation based on the U-Net convolutional neural network architecture. By cross-validating on more than 50, 000 annotated images, we demonstrate that all three approaches achieve much better accuracy than a current state-of-the-art method. Highest accuracy is achieved when the CNN is trained to segment the BM and RPE, and the drusen are detected by combining shortest path finding with polynomial fitting in a post-process.
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Notes
- 1.
The selective en face projection relies on an estimate of the RPE layer, which the direct drusen segmentation does not provide. Thus, only those steps of the FPE that do not rely on the en face could be applied in case of the direct segmentation.
References
Abràmoff, M.D., Garvin, M.K., Sonka, M.: Retinal imaging and image analysis. IEEE Rev. Biomed. Eng. 3, 169–208 (2010)
Chen, Q., Leng, T., Zheng, L., Kutzscher, L., Ma, J., de Sisternes, L., Rubin, D.L.: Automated drusen segmentation and quantification in SD-OCT images. Med. Image Anal. 17(8), 1058–1072 (2013)
Chiu, S.J., Izatt, J.A., O’Connell, R.V., Winter, K.P., Toth, C.A., Farsiu, S.: Validated automatic segmentation of AMD pathology including drusen and geographic atrophy in SD-OCT images. Invest. Opthalmol. Vis. Sci. 53(1), 53 (2012)
Farsiu, S., Chiu, S.J., Izatt, J.A., Toth, C.A.: Fast detection and segmentation of drusen in retinal optical coherence tomography images. In: Proceedings of SPIE, Ophthalmic Technologies XVIII, vol. 6844, p. 68440D (2008)
Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167 (2015)
Iwama, D., Hangai, M., Ooto, S., Sakamoto, A., Nakanishi, H., Fujimura, T., Domalpally, A., Danis, R.P., Yoshimura, N.: Automated assessment of drusen using three-dimensional spectral-domain optical coherence tomography. Invest. Ophthalmol. Vis. Sci. 53(3), 1576–1583 (2012)
Jager, R.D., Mieler, W.F., Miller, J.W.: Age-related macular degeneration. New Engl. J. Med. 358(24), 2606–2617 (2008)
Jain, N., Farsiu, S., Khanifar, A.A., Bearelly, S., Smith, R.T., Izatt, J.A., Toth, C.A.: Quantitative comparison of drusen segmented on SD-OCT versus drusen delineated on color fundus photographs. Invest. Ophthalmol. Vis. Sci. 51(10), 4875–4883 (2010)
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T.: Caffe: convolutional architecture for fast feature embedding. arXiv preprint arXiv:1408.5093 (2014)
Lee, C.S., Baughman, D.M., Lee, A.Y.: Deep learning is effective for classifying normal versus age-related macular degeneration optical coherence tomography images. Ophthalmol. Retina 1(4), 322–327 (2017)
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). doi:10.1007/978-3-319-24574-4_28
Schmidt-Erfurth, U., Bogunovic, H., Klimscha, S., Hu, X., Schlegl, T., Sadeghipour, A., Gerendas, B.S., Osborne, A., Waldstein, S.M.: Machine learning to predict the individual progression of AMD from imaging biomarkers. In: Proceedings of Association for Research in Vision and Ophthalmology, p. 3398 (2017)
de Sisternes, L., Simon, N., Tibshirani, R., Leng, T., Rubin, D.L.: Quantitative SD-OCT imaging biomarkers as indicators of age-related macular degeneration progressionpredicting AMD progression using SD-OCT features. Invest. Ophthalmol. Vis. Sci. 55(11), 7093–7103 (2014)
Sonka, M., Abràmoff, M.D.: Quantitative analysis of retinal OCT. Med. Image Anal. 33, 165–169 (2016)
Zheng, Y., Williams, B.M., Pratt, H., Al-Bander, B., Wu, X., Zhao, Y.: Computer aided diagnosis of age-related macular degeneration in 3D OCT images by deep learning. In: Proceedings of Association for Research in Vision and Ophthalmology, p. 824 (2017)
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Gorgi Zadeh, S. et al. (2017). CNNs Enable Accurate and Fast Segmentation of Drusen in Optical Coherence Tomography. In: Cardoso, M., et al. Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support . DLMIA ML-CDS 2017 2017. Lecture Notes in Computer Science(), vol 10553. Springer, Cham. https://doi.org/10.1007/978-3-319-67558-9_8
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