{"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/pansam-zero-shot-prompt-free-pancreas","title":"PanSAM: Zero-Shot, Prompt-Free Pancreas Segmentation in CT Imaging","arxiv_id":null,"date":"2024-07-03","proceeding":"ICML 2024 FM-Wild Workshop 2024 7","authors":["Abolfazl malekahmadi","Mohammad Taha Teimuri Jervakani","Armin Behnamnia","Zahra Dehghanian","Amir Shamloo","Hamid R. Rabiee"],"abstract":"Segmentation of the pancreas in CT images is crucial in multiple pancreatic diagnostic tasks, such as the detection, classification, and prognosis of pancreatic cancer. We present a segmentation model to find pancreatic tissue accurately in abdominal CT images. We utilize the Segment-Anything Model (SAM), a prompt-based 2D segmentation transformer model, and adapt it to 3D CT images to build a model that can segment the pancreas automatically without any prompts. To our knowledge, this is the first prompt-free work to segment the pancreas on a CT image based on the generalizable SAM model. We achieve a DICE score of 87.01% and a Jaccard score of 81.42% on the NIH dataset. We also performed zero-shot segmentation on the Abdominal-1K dataset. We achieved a DICE score of 83.20%, which shows the generalizability and applicability of our method to new unseen samples. Our study put together the zero-shot performance of SAM and the 3D nature of CT images to provide an automatic, real-time model that provides consistent segmentation throughout CT slices without the need for expert intervention.","url_abs":"https://openreview.net/forum?id=4pn1Enab5Q","url_pdf":"https://openreview.net/pdf?id=4pn1Enab5Q","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":"pansam-zero-shot-prompt-free-pancreas","repo_url":"https://github.com/teimuri/PSDDSAM","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"diagnostic","task_name":"Diagnostic"},{"task_slug":"pancreas-segmentation","task_name":"Pancreas Segmentation"},{"task_slug":"prognosis","task_name":"Prognosis"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"zero-shot-segmentation","task_name":"Zero Shot Segmentation"}],"methods":[{"method_slug":"sam","method_name":"SAM"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/pancreas-segmentation-on-pancreas-ct","task":"Pancreas Segmentation","dataset":"Pancreas-CT","model":"PanSAM","rank_in_archive_order":1,"of":1,"metrics":{"Dice":"87.01"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}