{"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/joint-segmentation-and-uncertainty","title":"Joint Segmentation and Uncertainty Visualization of Retinal Layers in Optical Coherence Tomography Images using Bayesian Deep Learning","arxiv_id":"1809.04282","date":"2018-09-12","proceeding":null,"authors":["Suman Sedai","Bhavna Antony","Dwarikanath Mahapatra","Rahil Garnavi"],"abstract":"Optical coherence tomography (OCT) is commonly used to analyze retinal layers\nfor assessment of ocular diseases. In this paper, we propose a method for\nretinal layer segmentation and quantification of uncertainty based on Bayesian\ndeep learning. Our method not only performs end-to-end segmentation of retinal\nlayers, but also gives the pixel wise uncertainty measure of the segmentation\noutput. The generated uncertainty map can be used to identify erroneously\nsegmented image regions which is useful in downstream analysis. We have\nvalidated our method on a dataset of 1487 images obtained from 15 subjects (OCT\nvolumes) and compared it against the state-of-the-art segmentation algorithms\nthat does not take uncertainty into account. The proposed uncertainty based\nsegmentation method results in comparable or improved performance, and most\nimportantly is more robust against noise.","url_abs":"http://arxiv.org/abs/1809.04282v1","url_pdf":"http://arxiv.org/pdf/1809.04282v1.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":"joint-segmentation-and-uncertainty","repo_url":"https://github.com/ssedai026/uncertainty-segmentation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"uncertainty-visualization","task_name":"Uncertainty Visualization"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}