{"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/iw-net-an-automatic-and-minimalistic","title":"iW-Net: an automatic and minimalistic interactive lung nodule segmentation deep network","arxiv_id":"1811.12789","date":"2018-11-30","proceeding":null,"authors":["Guilherme Aresta","Colin Jacobs","Teresa Araújo","António Cunha","Isabel Ramos","Bram van Ginneken","Aurélio Campilho"],"abstract":"We propose iW-Net, a deep learning model that allows for both automatic and\ninteractive segmentation of lung nodules in computed tomography images. iW-Net\nis composed of two blocks: the first one provides an automatic segmentation and\nthe second one allows to correct it by analyzing 2 points introduced by the\nuser in the nodule's boundary. For this purpose, a physics inspired weight map\nthat takes the user input into account is proposed, which is used both as a\nfeature map and in the system's loss function. Our approach is extensively\nevaluated on the public LIDC-IDRI dataset, where we achieve a state-of-the-art\nperformance of 0.55 intersection over union vs the 0.59 inter-observer\nagreement. Also, we show that iW-Net allows to correct the segmentation of\nsmall nodules, essential for proper patient referral decision, as well as\nimprove the segmentation of the challenging non-solid nodules and thus may be\nan important tool for increasing the early diagnosis of lung cancer.","url_abs":"http://arxiv.org/abs/1811.12789v1","url_pdf":"http://arxiv.org/pdf/1811.12789v1.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":"iw-net-an-automatic-and-minimalistic","repo_url":"https://github.com/gmaresta/iW-Net","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"interactive-segmentation","task_name":"Interactive Segmentation"},{"task_slug":"lung-nodule-segmentation","task_name":"Lung Nodule Segmentation"},{"task_slug":"segmentation","task_name":"Segmentation"}],"methods":[],"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}