{"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/abdominal-aortic-aneurysm-segmentation-with-a","title":"Abdominal Aortic Aneurysm Segmentation with a Small Number of Training Subjects","arxiv_id":"1804.02943","date":"2018-04-09","proceeding":null,"authors":["Jian-Qing Zheng","Xiao-Yun Zhou","Qing-Biao Li","Celia Riga","Guang-Zhong Yang"],"abstract":"Pre-operative Abdominal Aortic Aneurysm (AAA) 3D shape is critical for\ncustomized stent-graft design in Fenestrated Endovascular Aortic Repair\n(FEVAR). Traditional segmentation approaches implement expert-designed feature\nextractors while recent deep neural networks extract features automatically\nwith multiple non-linear modules. Usually, a large training dataset is\nessential for applying deep learning on AAA segmentation. In this paper, the\nAAA was segmented using U-net with a small number (two) of training subjects.\nFirstly, Computed Tomography Angiography (CTA) slices were augmented with gray\nvalue variation and translation to avoid the overfitting caused by the small\nnumber of training subjects. Then, U-net was trained to segment the AAA. Dice\nSimilarity Coefficients (DSCs) over 0.8 were achieved on the testing subjects.\nThe PLZ, DLZ and aortic branches are all reconstructed reasonably, which will\nfacilitate stent graft customization and help shape instantiation for\nintra-operative surgery navigation in FEVAR.","url_abs":"http://arxiv.org/abs/1804.02943v1","url_pdf":"http://arxiv.org/pdf/1804.02943v1.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":"abdominal-aortic-aneurysm-segmentation-with-a","repo_url":"https://github.com/jianqingzheng/path_planning_for_FEVAR","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"translation","task_name":"Translation"}],"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}