{"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/deep-segmentation-and-registration-in-x-ray","title":"Deep Segmentation and Registration in X-Ray Angiography Video","arxiv_id":"1805.06406","date":"2018-05-16","proceeding":null,"authors":["Athanasios Vlontzos","Krystian Mikolajczyk"],"abstract":"In interventional radiology, short video sequences of vein structure in\nmotion are captured in order to help medical personnel identify vascular issues\nor plan intervention. Semantic segmentation can greatly improve the usefulness\nof these videos by indicating exact position of vessels and instruments, thus\nreducing the ambiguity. We propose a real-time segmentation method for these\ntasks, based on U-Net network trained in a Siamese architecture from\nautomatically generated annotations. We make use of noisy low level binary\nsegmentation and optical flow to generate multi class annotations that are\nsuccessively improved in a multistage segmentation approach. We significantly\nimprove the performance of a state of the art U-Net at the processing speeds of\n90fps.","url_abs":"http://arxiv.org/abs/1805.06406v2","url_pdf":"http://arxiv.org/pdf/1805.06406v2.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":"deep-segmentation-and-registration-in-x-ray","repo_url":"https://github.com/thanosvlo/Deep-Segmentation-and-Registration-in-X-Ray-Angiography-Video","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"optical-flow-estimation","task_name":"Optical Flow Estimation"},{"task_slug":null,"task_name":"Position"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[{"method_slug":"concatenated-skip-connection","method_name":"Concatenated Skip Connection"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"u-net","method_name":"U-Net"}],"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}