{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,18]],"date-time":"2026-01-18T02:23:05Z","timestamp":1768702985181,"version":"3.49.0"},"reference-count":36,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2020,6,29]],"date-time":"2020-06-29T00:00:00Z","timestamp":1593388800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Age-Related Macular Degeneration (ARMD) is a progressive eye disease that slowly causes patients to go blind. For several years now, it has been an important research field to try to understand how the disease progresses and find effective medical treatments. Researchers have been mostly interested in studying the evolution of the lesions using different techniques ranging from manual annotation to mathematical models of the disease. However, artificial intelligence for ARMD image analysis has become one of the main research focuses to study the progression of the disease, as accurate manual annotation of its evolution has proved difficult using traditional methods even for experienced practicians. In this paper, we propose a deep learning architecture that can detect changes in the eye fundus images and assess the progression of the disease. Our method is based on joint autoencoders and is fully unsupervised. Our algorithm has been applied to pairs of images from different eye fundus images time series of 24 ARMD patients. Our method has been shown to be quite effective when compared with other methods from the literature, including non-neural network based algorithms that still are the current standard to follow the disease progression and change detection methods from other fields.<\/jats:p>","DOI":"10.3390\/jimaging6070057","type":"journal-article","created":{"date-parts":[[2020,6,29]],"date-time":"2020-06-29T11:17:17Z","timestamp":1593429437000},"page":"57","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Analyzing Age-Related Macular Degeneration Progression in Patients with Geographic Atrophy Using Joint Autoencoders for Unsupervised Change Detection"],"prefix":"10.3390","volume":"6","author":[{"given":"Guillaume","family":"Dupont","sequence":"first","affiliation":[{"name":"ISEP, DaSSIP Team, 92130 Issy-Les-Moulineaux, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8332-2491","authenticated-orcid":false,"given":"Ekaterina","family":"Kalinicheva","sequence":"additional","affiliation":[{"name":"ISEP, DaSSIP Team, 92130 Issy-Les-Moulineaux, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0508-8550","authenticated-orcid":false,"given":"J\u00e9r\u00e9mie","family":"Sublime","sequence":"additional","affiliation":[{"name":"ISEP, DaSSIP Team, 92130 Issy-Les-Moulineaux, France"},{"name":"Universit\u00e9 Paris 13, LIPN - CNRS UMR 7030, 93430 Villetaneuse, France"}]},{"given":"Florence","family":"Rossant","sequence":"additional","affiliation":[{"name":"ISEP, DaSSIP Team, 92130 Issy-Les-Moulineaux, France"}]},{"given":"Michel","family":"P\u00e2ques","sequence":"additional","affiliation":[{"name":"Clinical Imaging Center 1423, Quinze-Vingts Hospital, INSERM-DGOS Clinical Investigation Center, 75012 Paris, France"}]}],"member":"1968","published-online":{"date-parts":[[2020,6,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.preteyeres.2013.10.002","article-title":"Progress on retinal image analysis for age related macular degeneration","volume":"38","author":"Kanagasingam","year":"2014","journal-title":"Prog. Retin. Eye Res."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Priya, R., and Aruna, P. (2011, January 8\u201310). Automated diagnosis of Age-related macular degeneration from color retinal fundus images. Proceedings of the 2011 3rd International Conference on Electronics Computer Technology, Kanyakumari, India.","DOI":"10.1109\/ICECTECH.2011.5941690"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"611","DOI":"10.1016\/j.compbiomed.2008.02.008","article-title":"Automatic segmentation of age-related macular degeneration in retinal fundus images","volume":"38","author":"Sevik","year":"2008","journal-title":"Comput. Biol. Med."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1296","DOI":"10.1097\/IAE.0000000000000069","article-title":"Automated image alignment and segmentation to follow progression of geographic atrophy in age-related macular degeneration","volume":"34","author":"Ramsey","year":"2014","journal-title":"Retina"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10916-008-9210-4","article-title":"A Statistical Segmentation Method for Measuring Age-Related Macular Degeneration in Retinal Fundus Images","volume":"34","author":"Sevik","year":"2010","journal-title":"J. Med. Syst."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"116016","DOI":"10.1117\/1.3652709","article-title":"Retinal image restoration by means of blind deconvolution","volume":"16","author":"Marrugo","year":"2011","journal-title":"J. Biomed. Opt."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1016\/j.compbiomed.2015.06.018","article-title":"Automated segmentation of geographic atrophy of the retinal epithelium via random forests in AREDS color fundus images","volume":"65","author":"Feeny","year":"2015","journal-title":"Comput. Biol. Med."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Lee, N., Laine, A.F., and Smith, R.T. (2007, January 22\u201326). A hybrid segmentation approach for geographic atrophy in fundus auto-fluorescence images for diagnosis of age-related macular degeneration. Proceedings of the 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Lyon, France.","DOI":"10.1109\/IEMBS.2007.4353455"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"014501","DOI":"10.1117\/1.JMI.2.1.014501","article-title":"Automated segmentation of geographic atrophy in fundus autofluorescence images using supervised pixel classification","volume":"2","author":"Hu","year":"2015","journal-title":"J. Med. Imaging"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"8375","DOI":"10.1167\/iovs.13-12552","article-title":"Segmentation of the geographic atrophy in spectral-domain optical coherence tomography and fundus autofluorescence images","volume":"54","author":"Hu","year":"2013","journal-title":"Investig. Ophthalmol. Vis. Sci."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Troglio, G., Alberti, M., Benediktsson, J., Moser, G., Serpico, S., and Stef\u00e1nsson, E. (2010). Unsupervised Change-Detection in Retinal Images by a Multiple-Classifier Approach. International Workshop on Multiple Classifier Systems, Springer.","DOI":"10.1007\/978-3-642-12127-2_10"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Troglio, G., Nappo, A., Benediktsson, J., Moser, G., Serpico, S., and Stef\u00e1nsson, E. (2009, January 7\u201312). Automatic Change Detection of Retinal Images. Proceedings of the World Congress on Medical Physics and Biomedical Engineering, Munich, Germany.","DOI":"10.1007\/978-3-642-03891-4_75"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Hussain, M.A., Govindaiah, A., Souied, E., Smith, R., and Bhuiyan, A. (2018, January 25\u201329). Automated tracking and change detection for Age-related Macular Degeneration Progression using retinal fundus imaging. Proceedings of the 2018 Joint 7th International Conference on Informatics, Electronics & Vision (ICIEV) and 2018 2nd International Conference on Imaging, Vision & Pattern Recognition (icIVPR), Kitakyushu, Japan.","DOI":"10.1109\/ICIEV.2018.8641078"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Burlina, P., Freund, D.E., Joshi, N., Wolfson, Y., and Bressler, N.M. (2016, January 13\u201316). Detection of age-related macular degeneration via deep learning. Proceedings of the 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), Prague, Czech Republic.","DOI":"10.1109\/ISBI.2016.7493240"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1170","DOI":"10.1001\/jamaophthalmol.2017.3782","article-title":"Automated Grading of Age-Related Macular Degeneration From Color Fundus Images Using Deep Convolutional Neural Networks","volume":"135","author":"Burlina","year":"2017","journal-title":"JAMA Ophthalmol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"5893601","DOI":"10.1155\/2016\/5893601","article-title":"Automatic Screening and Grading of Age-Related Macular Degeneration from Texture Analysis of Fundus Images","volume":"2016","author":"Phan","year":"2016","journal-title":"J. Ophthalmol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.imu.2017.03.001","article-title":"Detection of small changes in medical and random-dot images comparing self-organizing map performance to human detection","volume":"7","author":"Wandeto","year":"2017","journal-title":"Inform. Med. Unlocked"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Kohonen, T. (1997). Self-Organizing Maps, Springer.","DOI":"10.1007\/978-3-642-97966-8"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Lee, N., Smith, R.T., and Laine, A.F. (2008, January 26\u201329). Interactive segmentation for geographic atrophy in retinal fundus images. Proceedings of the 2008 42nd Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, USA.","DOI":"10.1109\/ACSSC.2008.5074488"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Deckert, A., Schmitz-Valckenberg, S., Jorzik, J., Bindewald, A., Holz, F., and Mansmann, U. (2005). Automated analysis of digital fundus autofluorescence images of geographic atrophy in advanced age-related macular degeneration using confocal scanning laser ophthalmoscopy (cSLO). BMC Ophthalmol., 5.","DOI":"10.1186\/1471-2415-5-8"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"772","DOI":"10.1109\/LGRS.2009.2025059","article-title":"Unsupervised change detection in satellite images using principal component analysis and k-means clustering","volume":"6","author":"Celik","year":"2009","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Kalinicheva, E., Sublime, J., and Trocan, M. (2019). Change Detection in Satellite Images Using Reconstruction Errors of Joint Autoencoders. Artificial Neural Networks and Machine Learning\u2014ICANN 2019: Image, Processings of the 8th International Conference on Artificial Neural Networks, Munich, Germany, 17\u201319 September 2019, Springer. Proceedings, Part III; Lecture Notes in Computer Science 11729.","DOI":"10.1007\/978-3-030-30508-6_50"},{"key":"ref_23","first-page":"189","article-title":"Abnormal Event Detection in Videos Using Spatiotemporal Autoencoder","volume":"Volume 10262","author":"Cong","year":"2017","journal-title":"Advances in Neural Networks\u2014ISNN 2017, Proceedings of the 14th International Symposium, ISNN 2017, Sapporo, Hakodate, and Muroran, Hokkaido, Japan, 21\u201326 June 2017"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Kanezaki, A. (2018, January 15\u201320). Unsupervised Image Segmentation by Backpropagation. Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Calgary, AB, Canada.","DOI":"10.1109\/ICASSP.2018.8462533"},{"key":"ref_25","unstructured":"Xia, X., and Kulis, B. (2017). W-Net: A Deep Model for Fully Unsupervised Image Segmentation. arXiv."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Sublime, J., and Kalinicheva, E. (2019). Automatic Post-Disaster Damage Mapping Using Deep-Learning Techniques for Change Detection: Case Study of the Tohoku Tsunami. Remote Sens., 11.","DOI":"10.3390\/rs11091123"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1126\/science.1127647","article-title":"Reducing the Dimensionality of Data with Neural Networks","volume":"313","author":"Hinton","year":"2006","journal-title":"Science"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrell, T. (2014). Fully Convolutional Networks for Semantic Segmentation. arXiv.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"e453","DOI":"10.7717\/peerj.453","article-title":"scikit-image: Image processing in Python","volume":"2","author":"Boulogne","year":"2014","journal-title":"PeerJ"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.acha.2009.04.004","article-title":"MRA contextual-recovery extension of smooth functions on manifolds","volume":"28","author":"Chui","year":"2010","journal-title":"Appl. Comput. Harmon. Anal."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2018\/3950312","article-title":"On Surface Completion and Image Inpainting by Biharmonic Functions: Numerical Aspects","volume":"2018","author":"Damelin","year":"2018","journal-title":"Int. J. Math. Math. Sci."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1109\/TSMC.1979.4310076","article-title":"A Threshold Selection Method from Gray-Level Histograms","volume":"9","author":"Otsu","year":"1979","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2451","DOI":"10.1162\/089976600300015015","article-title":"Learning to Forget: Continual Prediction with LSTM","volume":"12","author":"Gers","year":"2000","journal-title":"Neural Comput."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Cho, K., van Merrienboer, B., G\u00fcl\u00e7ehre, \u00c7., Bahdanau, D., Bougares, F., Schwenk, H., and Bengio, Y. (2014, January 25\u201329). Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, EMNLP 2014, Doha, Qatar.","DOI":"10.3115\/v1\/D14-1179"},{"key":"ref_36","unstructured":"Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N.D., and Weinberger, K.Q. (2014). Generative Adversarial Nets. Advances in Neural Information Processing Systems 27, Proceedings of the Annual Conference on Neural Information Processing Systems 2014, Montreal, QC, Canada, 8\u201313 December 2014, MIT Press."}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/6\/7\/57\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:44:25Z","timestamp":1760175865000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/6\/7\/57"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,6,29]]},"references-count":36,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2020,7]]}},"alternative-id":["jimaging6070057"],"URL":"https:\/\/doi.org\/10.3390\/jimaging6070057","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,6,29]]}}}