title: MedSAM2: Segment Anything in 3D Medical Images and Videos
summary: Medical image and video segmentation is a critical task for precision
  medicine, which has witnessed considerable progress in developing task or
  modality-specific and generalist models for 2D images. However, there have been
  limited studies on building general-purpose models for 3D images and videos
  with comprehensive user studies. Here, we present MedSAM2, a promptable
  segmentation foundation model for 3D image and video segmentation. The model is
  developed by fine-tuning the Segment Anything Model 2 on a large medical
  dataset with over 455,000 3D image-mask pairs and 76,000 frames, outperforming
  previous models across a wide range of organs, lesions, and imaging modalities.
  Furthermore, we implement a human-in-the-loop pipeline to facilitate the
  creation of large-scale datasets resulting in, to the best of our knowledge,
  the most extensive user study to date, involving the annotation of 5,000 CT
  lesions, 3,984 liver MRI lesions, and 251,550 echocardiogram video frames,
  demonstrating that MedSAM2 can reduce manual costs by more than 85%. MedSAM2 is
  also integrated into widely used platforms with user-friendly interfaces for
  local and cloud deployment, making it a practical tool for supporting
  efficient, scalable, and high-quality segmentation in both research and
  healthcare environments.
paper_authors._id: 67f32c04be7fbc47d7ad7138
paper_authors.name: Jun Ma
paper_authors._id: 67f32c04be7fbc47d7ad7139
paper_authors.name: Zongxin Yang
paper_authors._id: 67f32c04be7fbc47d7ad713a
paper_authors.name: Sumin Kim
paper_authors._id: 67f32c04be7fbc47d7ad713b
paper_authors.name: Bihui Chen
paper_authors._id: 67f32c04be7fbc47d7ad713c
paper_authors.name: Mohammed Baharoon
paper_authors._id: 67f32c04be7fbc47d7ad713d
paper_authors.name: Adibvafa Fallahpour
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-08T06:53:21.041Z
paper_authors._id: 64c9888b3137cc529d0761c4
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/64c9888b3137cc529d0761c4/6n-_GMZqqssctlzuXy8H5.png
paper_authors.fullname: Adibvafa Fallahpour
paper_authors.type: user
paper_authors.user: adibvafa
paper_authors._id: 67f32c04be7fbc47d7ad713e
paper_authors.name: Reza Asakereh
paper_authors._id: 67f32c04be7fbc47d7ad713f
paper_authors.name: Hongwei Lyu
paper_authors._id: 67f32c04be7fbc47d7ad7140
paper_authors.name: Bo Wang
paper_title: MedSAM2: Segment Anything in 3D Medical Images and Videos
paper_summary: Medical image and video segmentation is a critical task for precision
  medicine, which has witnessed considerable progress in developing task or
  modality-specific and generalist models for 2D images. However, there have been
  limited studies on building general-purpose models for 3D images and videos
  with comprehensive user studies. Here, we present MedSAM2, a promptable
  segmentation foundation model for 3D image and video segmentation. The model is
  developed by fine-tuning the Segment Anything Model 2 on a large medical
  dataset with over 455,000 3D image-mask pairs and 76,000 frames, outperforming
  previous models across a wide range of organs, lesions, and imaging modalities.
  Furthermore, we implement a human-in-the-loop pipeline to facilitate the
  creation of large-scale datasets resulting in, to the best of our knowledge,
  the most extensive user study to date, involving the annotation of 5,000 CT
  lesions, 3,984 liver MRI lesions, and 251,550 echocardiogram video frames,
  demonstrating that MedSAM2 can reduce manual costs by more than 85%. MedSAM2 is
  also integrated into widely used platforms with user-friendly interfaces for
  local and cloud deployment, making it a practical tool for supporting
  efficient, scalable, and high-quality segmentation in both research and
  healthcare environments.
paper_ai_summary: MedSAM2 is a promptable 3D segmentation foundation model fine-tuned on a large medical dataset, outperforming previous models and reducing manual annotation costs by over 85%.
paper_ai_keywords: promptable segmentation foundation model Segment Anything Model 2 3D image and video segmentation large medical dataset human-in-the-loop pipeline user study CT lesions liver MRI lesions echocardiogram video frames manual annotation costs