{"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/cfun-combining-faster-r-cnn-and-u-net-network","title":"CFUN: Combining Faster R-CNN and U-net Network for Efficient Whole Heart Segmentation","arxiv_id":"1812.04914","date":"2018-12-12","proceeding":null,"authors":["Zhanwei Xu","Ziyi Wu","Jianjiang Feng"],"abstract":"In this paper, we propose a novel heart segmentation pipeline Combining\nFaster R-CNN and U-net Network (CFUN). Due to Faster R-CNN's precise\nlocalization ability and U-net's powerful segmentation ability, CFUN needs only\none-step detection and segmentation inference to get the whole heart\nsegmentation result, obtaining good results with significantly reduced\ncomputational cost. Besides, CFUN adopts a new loss function based on edge\ninformation named 3D Edge-loss as an auxiliary loss to accelerate the\nconvergence of training and improve the segmentation results. Extensive\nexperiments on the public dataset show that CFUN exhibits competitive\nsegmentation performance in a sharply reduced inference time. Our source code\nand the model are publicly available at https://github.com/Wuziyi616/CFUN.","url_abs":"http://arxiv.org/abs/1812.04914v1","url_pdf":"http://arxiv.org/pdf/1812.04914v1.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":"cfun-combining-faster-r-cnn-and-u-net-network","repo_url":"https://github.com/Wuziyi616/CFUN","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"heart-segmentation","task_name":"Heart Segmentation"},{"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":"faster-r-cnn","method_name":"Faster R-CNN"},{"method_slug":"rpn","method_name":"RPN"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"roipool","method_name":"RoIPool"},{"method_slug":"softmax","method_name":"Softmax"},{"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}