Abstract
Diabetic macular edema is a leading cause of visual loss for patients with diabetes. While diagnosis can only be performed by optical coherence tomography, diabetic macular edema risk assessment is often performed in eye fundus images in screening scenarios through the detection of hard exudates. Such screening scenarios are often associated with large amounts of data, high costs and high burden on specialists, motivating then the development of methodologies for automatic diabetic macular edema risk prediction. Nevertheless, significant dataset domain bias, due to different acquisition equipment, protocols and/or different populations can have significantly detrimental impact on the performance of automatic methods when transitioning to a new dataset, center or scenario. As such, in this study, a method based on residual neural networks is proposed for the classification of diabetic macular edema risk. This method is then validated across multiple public datasets, simulating the deployment in a multi-center setting and thereby studying the method’s generalization capability and existing dataset domain bias. Furthermore, the method is tested on a private dataset which more closely represents a realistic screening scenario. An average area under the curve across all public datasets of 0.891 ± 0.013 was obtained with a ResNet50 architecture trained on a limited amount of images from a single public dataset (IDRiD). It is also shown that screening scenarios are significantly more challenging and that training across multiple datasets leads to an improvement of performance (area under the curve of 0.911 ± 0.009).
C. Carvalho and J. Pedrosa—Equal contribution.
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
Abbasi-Sureshjani, S., Dashtbozorg, B., ter Haar Romeny, B.M., Fleuret, F.: Boosted exudate segmentation in retinal images using residual nets. In: Cardoso, M., et al. (eds.) Fetal, Infant and Ophthalmic Medical Image Analysis. Lecture Notes in Computer Science, vol. 10554, pp. 210–218. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-67561-9_24
Acharya, U.R., Mookiah, M.R.K., Koh, J.E., Tan, J.H., Bhandary, S.V., Rao, A.K., et al.: Automated diabetic macular edema (DME) grading system using DWT, DCT features and maculopathy index. Comput. Biol. Med. 84, 59–68 (2017)
Akram, M.U., Tariq, A., Khan, S.A., Javed, M.Y.: Automated detection of exudates and macula for grading of diabetic macular edema. Comput. Methods Programs Biomed. 114(2), 141–152 (2014)
Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, Cham (2006). https://doi.org/10.1007/978-1-4615-7566-5
Bradley, A.P.: The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recogn. 30(7), 1145–1159 (1997)
Bresnick, G.H., Mukamel, D.B., Dickinson, J.C., Cole, D.R.: A screening approach to the surveillance of patients with diabetes for the presence of vision-threatening retinopathy. Ophthalmology 107(1), 19–24 (2000)
Decencière, E., Cazuguel, G., Zhang, X., Thibault, G., Klein, J.C., Meyer, F., et al.: TeleOphta: machine learning and image processing methods for teleophthalmology. Irbm 34(2), 196–203 (2013)
Decencière, E., Zhang, X., Cazuguel, G., Lay, B., Cochener, B., Trone, C., et al.: Feedback on a publicly distributed image database: the Messidor database. Image Anal. Stereol. 33(3), 231–234 (2014)
Giancardo, L., Meriaudeau, F., Karnowski, T.P., Li, Y., Garg, S., Tobin Jr., K.W., et al.: Exudate-based diabetic macular edema detection in fundus images using publicly available datasets. Med. Image Anal. 16(1), 216–226 (2012)
Glorot, X., Bordes, A., Bengio, Y.: Deep sparse rectifier neural networks. In: 14th International Conference on Artificial Intelligence and Statistics, pp. 315–323 (2011)
Harangi, B., Hajdu, A.: Automatic exudate detection by fusing multiple active contours and regionwise classification. Comput. Biol. Med. 54, 156–171 (2014)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Hoover, A.: Structured Analysis of the Retina. https://www.cecas.clemson.edu/ahoover/stare
Imani, E., Pourreza, H.R.: A novel method for retinal exudate segmentation using signal separation algorithm. Comput. Methods Programs Biomed. 133, 195–205 (2016)
Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167 (2015)
Kälviäinen, R., Uusitalo, H.: DIARETDB1 diabetic retinopathy database and evaluation protocol. In: Medical Image Understanding and Analysis, vol. 2007, p. 61. Citeseer (2007)
Kauppi, T., Kalesnykiene, V., Kamarainen, J.K., Lensu, L., Sorri, I., Uusitalo, H., et al.: DIARETDB0: evaluation database and methodology for diabetic retinopathy algorithms. Mach. Vis. Pattern Recogn. Res. Group Lappeenranta Univ. Technol. Finl. 73, 1–17 (2006)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Lam, C., Yu, C., Huang, L., Rubin, D.: Retinal lesion detection with deep learning using image patches. Investig. Ophthalmol. Vis. Sci. 59(1), 590–596 (2018)
Lim, S., Zaki, W.M.D.W., Hussain, A., Lim, S., Kusalavan, S.: Automatic classification of diabetic macular edema in digital fundus images. In: 2011 IEEE Colloquium on Humanities, Science and Engineering, pp. 265–269. IEEE (2011)
Liu, Q., Zou, B., Chen, J., Ke, W., Yue, K., Chen, Z., et al.: A location-to-segmentation strategy for automatic exudate segmentation in colour retinal fundus images. Comput. Med. Imaging Graph. 55, 78–86 (2017)
Long, S., Huang, X., Chen, Z., Pardhan, S., Zheng, D.: Automatic detection of hard exudates in color retinal images using dynamic threshold and SVM classification: algorithm development and evaluation. BioMed Res. Int. 2019, 13 (2019)
Medeiros, M.D., Mesquita, E., Papoila, A.L., Genro, V., Raposo, J.F.: First diabetic retinopathy prevalence study in Portugal: RETINODIAB study - evaluation of the screening programme for Lisbon and Tagus Valley region. Br. J. Ophthalmol. 99(10), 1328–1333 (2015)
Mo, J., Zhang, L., Feng, Y.: Exudate-based diabetic macular edema recognition in retinal images using cascaded deep residual networks. Neurocomputing 290, 161–171 (2018)
Pires, R., Jelinek, H.F., Wainer, J., Valle, E., Rocha, A.: Advancing bag-of-visual-words representations for lesion classification in retinal images. PloS One 9(6), e96814 (2014)
Porwal, P., Pachade, S., Kamble, R., Kokare, M., Deshmukh, G., Sahasrabuddhe, V., et al.: Indian diabetic retinopathy image dataset (IDRiD). IEEE Dataport (2018)
Porwal, P., Pachade, S., Kamble, R., Kokare, M., Deshmukh, G., Sahasrabuddhe, V., et al.: Indian diabetic retinopathy image dataset (IDRiD): a database for diabetic retinopathy screening research. Data 3(3), 25 (2018)
Porwal, P., Pachade, S., Kokare, M., Deshmukh, G., Son, J., Bae, W., et al.: IDRiD: diabetic retinopathy-segmentation and grading challenge. Med. Image Anal. 59, 101561 (2020)
Prentašić, P., Lončarić, S., Vatavuk, Z., Benčić, G., Subašić, M., Petković, T., et al.: Diabetic retinopathy image database (DRiDB): a new database for diabetic retinopathy screening programs research. In: 2013 8th International Symposium on Image and Signal Processing and Analysis (ISPA), pp. 711–716. IEEE (2013)
Rahim, S.S., Palade, V., Almakky, I., Holzinger, A.: Detection of diabetic retinopathy and maculopathy in eye fundus images using deep learning and image augmentation. In: Holzinger, A., Kieseberg, P., Tjoa, A.M., Weippl, E. (eds.) CD-MAKE 2019. LNCS, vol. 11713, pp. 114–127. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-29726-8_8
Rekhi, R.S., Issac, A., Dutta, M.K., Travieso, C.M.: Automated classification of exudates from digital fundus images. In: 2017 International Conference and Workshop on Bioinspired Intelligence (IWOBI), pp. 1–6. IEEE (2017)
Russakovsky, O., et al.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211–252 (2015). https://doi.org/10.1007/s11263-015-0816-y
Schwartz, R., Dodge, J., Smith, N.A., Etzioni, O.: Green AI. arXiv preprint arXiv:1907.10597 (2019)
Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: visual explanations from deep networks via gradient-based localization. In: IEEE International Conference on Computer Vision, pp. 618–626 (2017)
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res/ 15(1), 1929–1958 (2014)
Tariq, A., Akram, M.U., Shaukat, A., Khan, S.A.: Automated detection and grading of diabetic maculopathy in digital retinal images. J. Digit. Imaging 26(4), 803–812 (2013)
Wanderley, D.S., Araújo, T., Carvalho, C.B., Maia, C., Penas, S., Carneiro, Â., et al.: Analysis of the performance of specialists and an automatic algorithm in retinal image quality assessment. In: 2019 IEEE 6th Portuguese Meeting on Bioengineering (ENBENG), pp. 1–4. IEEE (2019)
Zander, E., Herfurth, S., Bohl, B., Heinke, P., Herrmann, U., Kohnert, K.D., et al.: Maculopathy in patients with diabetes mellitus type 1 and type 2: associations with risk factors. Br. J. Ophthalmol. 84(8), 871–876 (2000)
Zhang, X., Thibault, G., Decencière, E., Marcotegui, B., Laÿ, B., Danno, R., et al.: Exudate detection in color retinal images for mass screening of diabetic retinopathy. Med. Image Anal. 18(7), 1026–1043 (2014)
Zhang, Y., Wu, H., Liu, H., Tong, L., Wang, M.D.: Improve model generalization and robustness to dataset bias with bias-regularized learning and domain-guided augmentation. arXiv preprint arXiv:1910.06745 (2019)
Acknowledgments
This work is financed by the ERDF - European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation - COMPETE 2020 Programme and by National Funds through the FCT Fundação para a Ciência e a Tecnologia within project CMUP-ERI/TIC/0028/2014.
The Messidor database was kindly provided by the Messidor program partners (see http://www.adcis.net/en/third-party/messidor/).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Carvalho, C. et al. (2020). A Multi-dataset Approach for DME Risk Detection in Eye Fundus Images. In: Campilho, A., Karray, F., Wang, Z. (eds) Image Analysis and Recognition. ICIAR 2020. Lecture Notes in Computer Science(), vol 12132. Springer, Cham. https://doi.org/10.1007/978-3-030-50516-5_25
Download citation
DOI: https://doi.org/10.1007/978-3-030-50516-5_25
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-50515-8
Online ISBN: 978-3-030-50516-5
eBook Packages: Computer ScienceComputer Science (R0)