title: Medical SAM 2: Segment medical images as video via Segment Anything
    Model 2
summary: In this paper, we introduce Medical SAM 2 (MedSAM-2), an advanced
  segmentation model that utilizes the SAM 2 framework to address both 2D and 3D
  medical image segmentation tasks. By adopting the philosophy of taking medical
  images as videos, MedSAM-2 not only applies to 3D medical images but also
  unlocks new One-prompt Segmentation capability. That allows users to provide a
  prompt for just one or a specific image targeting an object, after which the
  model can autonomously segment the same type of object in all subsequent
  images, regardless of temporal relationships between the images. We evaluated
  MedSAM-2 across a variety of medical imaging modalities, including abdominal
  organs, optic discs, brain tumors, thyroid nodules, and skin lesions, comparing
  it against state-of-the-art models in both traditional and interactive
  segmentation settings. Our findings show that MedSAM-2 not only surpasses
  existing models in performance but also exhibits superior generalization across
  a range of medical image segmentation tasks. Our code will be released at:
  https://github.com/MedicineToken/Medical-SAM2
paper_authors._id: 66b043e987f605ac4dabcad1
paper_authors.name: Jiayuan Zhu
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2025-02-28T14:12:11.497Z
paper_authors._id: 66aff1d8ccc0fb3883dd19a8
paper_authors.avatarUrl: /avatars/dc9a0c622f0509c5bc9bf82d8f6ad7e3.svg
paper_authors.fullname: Jiayuan Zhu
paper_authors.type: user
paper_authors.user: jiayuanz3
paper_authors._id: 66b043e987f605ac4dabcad2
paper_authors.name: Yunli Qi
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-08-05T08:03:37.405Z
paper_authors._id: 65733dc5addd9129ff1fc477
paper_authors.avatarUrl: /avatars/834df10741464101e53933692ade68a7.svg
paper_authors.fullname: Yunli Qi
paper_authors.type: user
paper_authors.user: yunliqi
paper_authors._id: 66b043e987f605ac4dabcad3
paper_authors.name: Junde Wu
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2024-08-11T12:53:12.923Z
paper_authors._id: 6317257fc92fd6fee317ff7c
paper_authors.avatarUrl: /avatars/2f460a2f28562c987becb2acad8d93e7.svg
paper_authors.fullname: Junde Wu
paper_authors.type: user
paper_authors.user: morson
paper_title: Medical SAM 2: Segment medical images as video via Segment Anything
    Model 2
paper_summary: In this paper, we introduce Medical SAM 2 (MedSAM-2), an advanced
  segmentation model that utilizes the SAM 2 framework to address both 2D and 3D
  medical image segmentation tasks. By adopting the philosophy of taking medical
  images as videos, MedSAM-2 not only applies to 3D medical images but also
  unlocks new One-prompt Segmentation capability. That allows users to provide a
  prompt for just one or a specific image targeting an object, after which the
  model can autonomously segment the same type of object in all subsequent
  images, regardless of temporal relationships between the images. We evaluated
  MedSAM-2 across a variety of medical imaging modalities, including abdominal
  organs, optic discs, brain tumors, thyroid nodules, and skin lesions, comparing
  it against state-of-the-art models in both traditional and interactive
  segmentation settings. Our findings show that MedSAM-2 not only surpasses
  existing models in performance but also exhibits superior generalization across
  a range of medical image segmentation tasks. Our code will be released at:
  https://github.com/MedicineToken/Medical-SAM2
paper_ai_summary: MedSAM-2 extends the SAM 2 framework for 2D and 3D medical image segmentation, offering one-prompt segmentation across multiple images and outperforming existing models in various medical imaging tasks.
paper_ai_keywords: SAM 2 Medical SAM 2 MedSAM-2 video-based segmentation One-prompt Segmentation medical imaging modalities abdominal organs optic discs brain tumors thyroid nodules skin lesions interactive segmentation