{"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/automatic-detection-of-lumen-and-media-in-the","title":"Automatic detection of lumen and media in the IVUS images using U-Net with VGG16 Encoder","arxiv_id":"1806.07554","date":"2018-06-20","proceeding":null,"authors":["Chirag Balakrishna","Sarshar Dadashzadeh","Sara Soltaninejad"],"abstract":"Coronary heart disease is one of the top rank leading cause of mortality in\nthe world which can be because of plaque burden inside the arteries.\nIntravascular Ultrasound (IVUS) has been recognized as power- ful imaging\ntechnology which captures the real time and high resolution images of the\ncoronary arteries and can be used for the analysis of these plaques. The IVUS\nsegmentation involves the extraction of two arterial walls components namely,\nlumen and media. In this paper, we investi- gate the effectiveness of\nConvolutional Neural Networks including U-Net to segment ultrasound scans of\narteries. In particular, the proposed seg- mentation network was built based on\nthe the U-Net with the VGG16 encoder. Experiments were done for evaluating the\nproposed segmen- tation architecture which show promising quantitative and\nqualitative results.","url_abs":"http://arxiv.org/abs/1806.07554v1","url_pdf":"http://arxiv.org/pdf/1806.07554v1.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":"automatic-detection-of-lumen-and-media-in-the","repo_url":"https://github.com/dorltcheng/Transfer-Learning-U-Net-Deep-Learning-for-Lung-Ultrasound-Segmentation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[],"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}