{"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/cerebrovascular-network-segmentation-on-mra","title":"Cerebrovascular Network Segmentation on MRA Images with Deep Learning","arxiv_id":"1812.01752","date":"2018-12-04","proceeding":null,"authors":["Pedro Sanches","Cyril Meyer","Vincent Vigon","Benoît Naegel"],"abstract":"Deep learning has been shown to produce state of the art results in many\ntasks in biomedical imaging, especially in segmentation. Moreover, segmentation\nof the cerebrovascular structure from magnetic resonance angiography is a\nchallenging problem because its complex geometry and topology have a large\ninter-patient variability. Therefore, in this work, we present a convolutional\nneural network approach for this problem. Particularly, a new network topology\ninspired by the U-net 3D and by the Inception modules, entitled Uception. In\naddition, a discussion about the best objective function for sparse data also\nguided most choices during the project. State of the art models are also\nimplemented for a comparison purpose and final results show that the proposed\narchitecture has the best performance in this particular context.","url_abs":"http://arxiv.org/abs/1812.01752v1","url_pdf":"http://arxiv.org/pdf/1812.01752v1.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":"cerebrovascular-network-segmentation-on-mra","repo_url":"https://github.com/SANCHES-Pedro/Segmentation_3D_DeepLearning","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"cerebrovascular-network-segmentation","task_name":"Cerebrovascular Network Segmentation"},{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"segmentation","task_name":"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}