Abstract
The retinal vasculature imaged with fundus photography has the potential of encoding precious information for image-based retinal biomarkers, however, progress in their development is slow due to the need of defining vasculature morphology variables a priori and developing algorithms specific to these variables. In this paper, we introduce a novel approach to learn a general descriptor (or embedding) that captures the vasculature morphology in a numerically compact vector with minimal feature engineering. The vasculature embedding is computed by leveraging the internal representation of a new encoder-enhanced fully convolutional neural network, trained end-to-end with the raw pixels and manually segmented vessels. This approach effectively transfers the vasculature patterns learned by the network into a general purpose vasculature embedding vector. Using Messidor and Messidor-2, two publicly available datasets, we test the vasculature embeddings on two tasks: (1) an image retrieval task, which retrieved similar images according to their vasculature; (2) a diabetic retinopathy classification task, where we show how the vasculature embeddings improve the classification of an algorithm based on microaneurysms detection by 0.04 AUC on average.
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References
MacGillivray, T.J., Cameron, J.R., Zhang, Q., El-Medany, A., Mulholland, C., Sheng, Z., Dhillon, B., Doubal, F.N., Foster, P.J., Trucco, E., Sudlow, C.: Suitability of UK biobank retinal images for automatic analysis of morphometric properties of the vasculature. PLoS ONE 10(5), 1–10 (2015)
Trucco, E., Giachetti, A., Ballerini, L., Relan, D., Cavinato, A., MacGillivray, T.: Morphometric measurements of the retinal vasculature in fundus images with vampire. In: Biomedical Image Understanding: Methods and Applications, pp. 91–111 (2015)
Fiorin, D., Ruggeri, A.: Computerized analysis of narrow-field ROP images for the assessment of vessel caliber and tortuosity. In: Proceedings of EMBS, pp. 2622–2625 (2011)
Xu, X., Niemeijer, M., Song, Q., Sonka, M., Garvin, M.K., Reinhardt, J.M., Abramoff, M.D.: Vessel boundary delineation on fundus images using graph-based approach. IEEE Trans. Med. Imaging 30(6), 1184–1191 (2011)
Azemin, M.Z.C., Kumar, D.K., Wong, T.Y., Kawasaki, R., Mitchell, P., Wang, J.J.: Robust methodology for fractal analysis of the retinal vasculature. IEEE Trans. Med. Imaging 30(2), 243–250 (2011)
Bengio, Y., Courville, A., Vincent, P.: Representation learning a review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1798–828 (2013)
Fu, H., Xu, Y., Lin, S., Kee Wong, D.W., Liu, J.: DeepVessel: Retinal vessel segmentation via deep learning and conditional random field. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 132–139. Springer, Cham (2016). doi:10.1007/978-3-319-46723-8_16
Maninis, K.-K., Pont-Tuset, J., Arbeláez, P., Van Gool, L.: Deep retinal image understanding. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 140–148. Springer, Cham (2016). doi:10.1007/978-3-319-46723-8_17
Pizer, S.M., Amburn, E.P., Austin, J.D., Cromartie, R., Geselowitz, A., Greer, T., Ter Haar Romeny, B., Zimmerman, J.B., Zuiderveld, K.: Adaptive histogram equalization and its variations. Comput. Vis. Graph. Image Process. 39(3), 355–368 (1987)
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
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of CVPR, pp. 3431–3440 (2015)
Staal, J., Abramoff, M.D., Niemeijer, M., Viergever, M.A., van Ginneken, B.: Ridge-based vessel segmentation in color images of the retina. IEEE Trans. Med. Imaging 23(4), 501–509 (2004)
Decencière, E., Zhang, X., Cazuguel, G., Laÿ, B., Cochener, B., Trone, C., Gain, P., Ordóñez-Varela, J.R., Massin, P., Erginay, A., Charton, B., Klein, J.C.: Feedback on a publicly distributed image database: The Messidor database. Image Anal. Stereology 33(3), 231–234 (2014)
Giancardo, L., Meriaudeau, F., Karnowski, T.P., Tobin, K.W., Chaum, E.: Validation of microaneurysm-based diabetic retinopathy screening across retina fundus datasets. In: Proceedings of CBMS, pp. 125–130 (2013)
Acknowledgement
This work has been supported by the Center for Precision Health and School of Biomedical Informatics at University of Texas Health Science Center at Houston. We would like to thank Daniele Cortinovis for the initial implementation of U-Net on https://github.com/orobix/retina-unet. The Messidor and Messidor-2 datasets are kindly provided by the LaTIM laboratory (see http://latim.univ-brest.fr/) and the Messidor program partners (see http://messidor.crihan.fr/)”.
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Giancardo, L., Roberts, K., Zhao, Z. (2017). Representation Learning for Retinal Vasculature Embeddings. In: Cardoso, M., et al. Fetal, Infant and Ophthalmic Medical Image Analysis. OMIA FIFI 2017 2017. Lecture Notes in Computer Science(), vol 10554. Springer, Cham. https://doi.org/10.1007/978-3-319-67561-9_28
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