{"ID":2837521,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.19046","arxiv_id":"2511.19046","title":"MedSAM3: Delving into Segment Anything with Medical Concepts","abstract":"Medical image segmentation is fundamental for biomedical discovery. Existing methods lack generalizability and demand extensive, time-consuming manual annotation for new clinical application. Here, we propose MedSAM-3, a text promptable medical segmentation model for medical image and video segmentation. By fine-tuning the Segment Anything Model (SAM) 3 architecture on medical images paired with semantic conceptual labels, our MedSAM-3 enables medical Promptable Concept Segmentation (PCS), allowing precise targeting of anatomical structures via open-vocabulary text descriptions rather than solely geometric prompts. We further introduce the MedSAM-3 Agent, a framework that integrates Multimodal Large Language Models (MLLMs) to perform complex reasoning and iterative refinement in an agent-in-the-loop workflow. Comprehensive experiments across diverse medical imaging modalities, including X-ray, MRI, Ultrasound, CT, and video, demonstrate that our approach significantly outperforms existing specialist and foundation models. We will release our code and model at https://github.com/Joey-S-Liu/MedSAM3.","short_abstract":"Medical image segmentation is fundamental for biomedical discovery. Existing methods lack generalizability and demand extensive, time-consuming manual annotation for new clinical application. Here, we propose MedSAM-3, a text promptable medical segmentation model for medical image and video segmentation. By fine-tuning...","url_abs":"https://arxiv.org/abs/2511.19046","url_pdf":"https://arxiv.org/pdf/2511.19046v1","authors":"[\"Anglin Liu\",\"Rundong Xue\",\"Xu R. Cao\",\"Yifan Shen\",\"Yi Lu\",\"Xiang Li\",\"Qianqian Chen\",\"Jintai Chen\"]","published":"2025-11-24T12:34:38Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.AI\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false,"code_links":[{"ID":606699,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2837521,"paper_url":"https://arxiv.org/abs/2511.19046","paper_title":"MedSAM3: Delving into Segment Anything with Medical Concepts","repo_url":"https://github.com/Joey-S-Liu/MedSAM3","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}