{"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/semantic-context-forests-for-learning-based","title":"Semantic Context Forests for Learning-Based Knee Cartilage Segmentation in 3D MR Images","arxiv_id":"1307.2965","date":"2013-07-11","proceeding":null,"authors":["Quan Wang","Dijia Wu","Le Lu","Meizhu Liu","Kim L. Boyer","Shaohua Kevin Zhou"],"abstract":"The automatic segmentation of human knee cartilage from 3D MR images is a\nuseful yet challenging task due to the thin sheet structure of the cartilage\nwith diffuse boundaries and inhomogeneous intensities. In this paper, we\npresent an iterative multi-class learning method to segment the femoral, tibial\nand patellar cartilage simultaneously, which effectively exploits the spatial\ncontextual constraints between bone and cartilage, and also between different\ncartilages. First, based on the fact that the cartilage grows in only certain\narea of the corresponding bone surface, we extract the distance features of not\nonly to the surface of the bone, but more informatively, to the densely\nregistered anatomical landmarks on the bone surface. Second, we introduce a set\nof iterative discriminative classifiers that at each iteration, probability\ncomparison features are constructed from the class confidence maps derived by\npreviously learned classifiers. These features automatically embed the semantic\ncontext information between different cartilages of interest. Validated on a\ntotal of 176 volumes from the Osteoarthritis Initiative (OAI) dataset, the\nproposed approach demonstrates high robustness and accuracy of segmentation in\ncomparison with existing state-of-the-art MR cartilage segmentation methods.","url_abs":"http://arxiv.org/abs/1307.2965v2","url_pdf":"http://arxiv.org/pdf/1307.2965v2.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":"semantic-context-forests-for-learning-based","repo_url":"https://github.com/wq2012/DecisionForest","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"3d-medical-imaging-segmentation","task_name":"3D Medical Imaging Segmentation"},{"task_slug":"segmentation","task_name":"Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}