{"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/how-distance-transform-maps-boost","title":"How Distance Transform Maps Boost Segmentation CNNs: An Empirical Study","arxiv_id":null,"date":"2020-01-25","proceeding":"MIDL 2019 7","authors":["Jun Ma","Zhan Wei","Yiwen Zhang","Yixin Wang","Rongfei Lv","Cheng Zhu","Gaoxiang Chen","Jianan Liu","Chao Peng","Lei Wang","Yunpeng Wang","Jianan Chen"],"abstract":"Incorporating distance transform maps of ground truth into segmentation CNNs has been an interesting new trend in the last year. Despite many great works leading to improvements in a variety of segmentation tasks, the comparison among these methods has not been well studied.\nIn this paper, our \\emph{first contribution} is to summarize the latest developments of these methods in the 3D medical segmentation field.\nThe \\emph{second contribution} is that we systematically evaluated five benchmark methods on two representative public datasets.\nThese experiments highlight that all the five benchmark methods can bring performance gains to baseline V-Net. However, the implementation details have a noticeable impact on the performance, and not all the methods hold the benefits on different datasets.\nFinally, we suggest the best practices and indicate unsolved problems for incorporating distance transform maps into CNNs, which we hope would be useful for the community. The codes and trained models are publicly available at \\url{https://github.com/JunMa11/SegWithDistMap}.","url_abs":"https://openreview.net/forum?id=hM4pNbXWst","url_pdf":"https://openreview.net/pdf?id=hM4pNbXWst","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":"how-distance-transform-maps-boost","repo_url":"https://github.com/JunMa11/SegWithDistMap","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"how-distance-transform-maps-boost","repo_url":"https://github.com/LIVIAETS/surface-loss","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"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}