{"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/3d-path-planning-from-a-single-2d","title":"3D Path Planning from a Single 2D Fluoroscopic Image for Robot Assisted Fenestrated Endovascular Aortic Repair","arxiv_id":"1809.05955","date":"2018-09-16","proceeding":null,"authors":["Jian-Qing Zheng","Xiao-Yun Zhou","Celia Riga","Guang-Zhong Yang"],"abstract":"The current standard of intra-operative navigation during Fenestrated\nEndovascular Aortic Repair (FEVAR) calls for need of 3D alignments between\ninserted devices and aortic branches. The navigation commonly via 2D\nfluoroscopic images, lacks anatomical information, resulting in longer\noperation hours and radiation exposure. In this paper, a framework for\nreal-time 3D robotic path planning from a single 2D fluoroscopic image of\nAbdominal Aortic Aneurysm (AAA) is introduced. A graph matching method is\nproposed to establish the correspondence between the 3D preoperative and 2D\nintra-operative AAA skeletons, and then the two skeletons are registered by\nskeleton deformation and regularization in respect to skeleton length and\nsmoothness. Furthermore, deep learning was used to segment 3D pre-operative AAA\nfrom Computed Tomography (CT) scans to facilitate the framework automation.\nSimulation, phantom and patient AAA data sets have been used to validate the\nproposed framework. 3D distance error of 2mm was achieved in the phantom setup.\nPerformance advantages were also achieved in terms of accuracy, robustness and\ntime-efficiency. All the code will be open source.","url_abs":"http://arxiv.org/abs/1809.05955v1","url_pdf":"http://arxiv.org/pdf/1809.05955v1.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":"3d-path-planning-from-a-single-2d","repo_url":"https://github.com/jianqingzheng/path_planning_for_FEVAR","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"computed-tomography-ct","task_name":"Computed Tomography (CT)"},{"task_slug":"graph-matching","task_name":"Graph Matching"},{"task_slug":"image-to-point-cloud-registration","task_name":"Image to Point Cloud Registration"}],"methods":[{"method_slug":"repair","method_name":"Repair"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}