{"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/x-ray-transform-invariant-anatomical-landmark","title":"X-ray-transform Invariant Anatomical Landmark Detection for Pelvic Trauma Surgery","arxiv_id":"1803.08608","date":"2018-03-22","proceeding":null,"authors":["Bastian Bier","Mathias Unberath","Jan-Nico Zaech","Javad Fotouhi","Mehran Armand","Greg Osgood","Nassir Navab","Andreas Maier"],"abstract":"X-ray image guidance enables percutaneous alternatives to complex procedures.\nUnfortunately, the indirect view onto the anatomy in addition to projective\nsimplification substantially increase the task-load for the surgeon. Additional\n3D information such as knowledge of anatomical landmarks can benefit surgical\ndecision making in complicated scenarios. Automatic detection of these\nlandmarks in transmission imaging is challenging since image-domain features\ncharacteristic to a certain landmark change substantially depending on the\nviewing direction. Consequently and to the best of our knowledge, the above\nproblem has not yet been addressed. In this work, we present a method to\nautomatically detect anatomical landmarks in X-ray images independent of the\nviewing direction. To this end, a sequential prediction framework based on\nconvolutional layers is trained on synthetically generated data of the pelvic\nanatomy to predict 23 landmarks in single X-ray images. View independence is\ncontingent on training conditions and, here, is achieved on a spherical segment\ncovering (120 x 90) degrees in LAO/RAO and CRAN/CAUD, respectively, centered\naround AP. On synthetic data, the proposed approach achieves a mean prediction\nerror of 5.6 +- 4.5 mm. We demonstrate that the proposed network is immediately\napplicable to clinically acquired data of the pelvis. In particular, we show\nthat our intra-operative landmark detection together with pre-operative CT\nenables X-ray pose estimation which, ultimately, benefits initialization of\nimage-based 2D/3D registration.","url_abs":"http://arxiv.org/abs/1803.08608v1","url_pdf":"http://arxiv.org/pdf/1803.08608v1.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":"x-ray-transform-invariant-anatomical-landmark","repo_url":"https://github.com/arcadelab/DeepDRR","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"x-ray-transform-invariant-anatomical-landmark","repo_url":"https://github.com/mathiasunberath/DeepDRR","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"anatomical-landmark-detection","task_name":"Anatomical Landmark Detection"},{"task_slug":"anatomy","task_name":"Anatomy"},{"task_slug":"decision-making","task_name":"Decision Making"},{"task_slug":"pose-estimation","task_name":"Pose Estimation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1803.08608","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}