{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T22:15:53Z","timestamp":1772835353712,"version":"3.50.1"},"reference-count":31,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2021,8,11]],"date-time":"2021-08-11T00:00:00Z","timestamp":1628640000000},"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), a major cause of sight impairment for elderly people, is still not well understood despite intensive research. Measuring the size of the lesions in the fundus is the main biomarker of the severity of the disease and as such is widely used in clinical trials yet only relies on manual segmentation. Artificial intelligence, in particular automatic image analysis based on neural networks, has a major role to play in better understanding the disease, by analyzing the intrinsic optical properties of dry ARMD lesions from patient images. In this paper, we propose a comparison of automatic segmentation methods (classical computer vision method, machine learning method and deep learning method) in an unsupervised context applied on cSLO IR images. Among the methods compared, we propose an adaptation of a fully convolutional network, called W-net, as an efficient method for the segmentation of ARMD lesions. Unlike supervised segmentation methods, our algorithm does not require annotated data which are very difficult to obtain in this application. Our method was tested on a dataset of 328 images and has shown to reach higher quality results than other compared unsupervised methods with a F1 score of 0.87, while having a more stable model, even though in some specific cases, texture\/edges-based methods can produce relevant results.<\/jats:p>","DOI":"10.3390\/jimaging7080143","type":"journal-article","created":{"date-parts":[[2021,8,11]],"date-time":"2021-08-11T21:48:12Z","timestamp":1628718492000},"page":"143","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Unsupervised Approaches for the Segmentation of Dry ARMD Lesions in Eye Fundus cSLO Images"],"prefix":"10.3390","volume":"7","author":[{"given":"Cl\u00e9ment","family":"Royer","sequence":"first","affiliation":[{"name":"ISEP\u2014School of Digital Engineers, 92130 Issy-Les-Moulineaux, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0508-8550","authenticated-orcid":false,"given":"J\u00e9r\u00e9mie","family":"Sublime","sequence":"additional","affiliation":[{"name":"ISEP\u2014School of Digital Engineers, 92130 Issy-Les-Moulineaux, France"},{"name":"LIPN\u2014CNRS UMR 7030, LaMSN\u2014Universit\u00e9 Sorbonne Paris Nord, 93210 St Denis, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2517-5213","authenticated-orcid":false,"given":"Florence","family":"Rossant","sequence":"additional","affiliation":[{"name":"ISEP\u2014School of Digital Engineers, 92130 Issy-Les-Moulineaux, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michel","family":"Paques","sequence":"additional","affiliation":[{"name":"Clinical Imaging Center 1423, Quinze-Vingts Hospital, INSERM-DGOS Clinical Investigation Center, 75012 Paris, France"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,11]]},"reference":[{"key":"ref_1","unstructured":"Platanios, E.A., Al-Shedivat, M., Xing, E., and Mitchell, T. 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