{"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/autopet-challenge-2022-automatic-segmentation","title":"AutoPET Challenge 2022: Automatic Segmentation of Whole-body Tumor Lesion Based on Deep Learning and FDG PET/CT","arxiv_id":"2209.01212","date":"2022-08-31","proceeding":null,"authors":["Shaonan Zhong","Junyang Mo","Zhantao Liu"],"abstract":"Automatic segmentation of tumor lesions is a critical initial processing step for quantitative PET/CT analysis. However, numerous tumor lesion with different shapes, sizes, and uptake intensity may be distributed in different anatomical contexts throughout the body, and there is also significant uptake in healthy organs. Therefore, building a systemic PET/CT tumor lesion segmentation model is a challenging task. In this paper, we propose a novel training strategy to build deep learning models capable of systemic tumor segmentation. Our method is validated on the training set of the AutoPET 2022 Challenge. We achieved 0.7574 Dice score, 0.0299 false positive volume and 0.2538 false negative volume on preliminary test set.The code of our work is available on the following link: https://github.com/ZZZsn/MICCAI2022-autopet.","url_abs":"https://arxiv.org/abs/2209.01212v1","url_pdf":"https://arxiv.org/pdf/2209.01212v1.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":"autopet-challenge-2022-automatic-segmentation","repo_url":"https://github.com/zzzsn/miccai2022-autopet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"lesion-segmentation","task_name":"Lesion Segmentation"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"tumor-segmentation","task_name":"Tumor Segmentation"}],"methods":[{"method_slug":"test","method_name":"Test"}],"datasets_introduced":[{"slug":"autopet","name":"AutoPET","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}