{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/relaynet-retinal-layer-and-fluid-segmentation","title":"ReLayNet: Retinal Layer and Fluid Segmentation of Macular Optical Coherence Tomography using Fully Convolutional Network","arxiv_id":"1704.02161","date":"2017-04-07","proceeding":null,"authors":["Abhijit Guha Roy","Sailesh Conjeti","Sri Phani Krishna Karri","Debdoot Sheet","Amin Katouzian","Christian Wachinger","Nassir Navab"],"abstract":"Optical coherence tomography (OCT) is used for non-invasive diagnosis of\ndiabetic macular edema assessing the retinal layers. In this paper, we propose\na new fully convolutional deep architecture, termed ReLayNet, for end-to-end\nsegmentation of retinal layers and fluid masses in eye OCT scans. ReLayNet uses\na contracting path of convolutional blocks (encoders) to learn a hierarchy of\ncontextual features, followed by an expansive path of convolutional blocks\n(decoders) for semantic segmentation. ReLayNet is trained to optimize a joint\nloss function comprising of weighted logistic regression and Dice overlap loss.\nThe framework is validated on a publicly available benchmark dataset with\ncomparisons against five state-of-the-art segmentation methods including two\ndeep learning based approaches to substantiate its effectiveness.","url_abs":"http://arxiv.org/abs/1704.02161v2","url_pdf":"http://arxiv.org/pdf/1704.02161v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"relaynet-retinal-layer-and-fluid-segmentation","repo_url":"https://github.com/Nikolay1998/relaynet_pytorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"relaynet-retinal-layer-and-fluid-segmentation","repo_url":"https://github.com/abhi4ssj/relaynet_pytorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[{"method_slug":"logistic-regression","method_name":"Logistic Regression"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}