README.md

metadata

datasets:
  - medical
language: en
library_name: torch
license: cc-by-sa-4.0
pipeline_tag: image-segmentation
tags:
  - medical
  - segmentation
  - sam
  - medical-imaging
  - ct
  - mri
  - ultrasound

MedSAM2: Segment Anything in 3D Medical Images and Videos

Project Page

Authors

Jun Ma^{* 1,2}, Zongxin Yang^{* 3}, Sumin Kim^2,4,5, Bihui Chen^2,4,5, Mohammed Baharoon^2,3,5,
Adibvafa Fallahpour^2,4,5, Reza Asakereh^4,7, Hongwei Lyu⁴, Bo Wang^{† 1,2,4,5,6}

^* Equal contribution     ^† Corresponding author

¹AI Collaborative Centre, University Health Network, Toronto, Canada
²Vector Institute for Artificial Intelligence, Toronto, Canada
³Department of Biomedical Informatics, Harvard Medical School, Harvard University, Boston, USA
⁴Peter Munk Cardiac Centre, University Health Network, Toronto, Canada
⁵Department of Computer Science, University of Toronto, Toronto, Canada
⁶Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Canada
⁷Roche Canada and Genentech

Highlights

• A promptable foundation model for 3D medical image and video segmentation
• Trained on 455,000+ 3D image-mask pairs and 76,000+ annotated video frames
• Versatile segmentation capability across diverse organs and pathologies
• Extensive user studies in large-scale lesion and video datasets demonstrate that MedSAM2 substantially facilitates annotation workflows

Model Overview

MedSAM2 is a promptable segmentation segmentation model tailored for medical imaging applications. Built upon the foundation of the Segment Anything Model (SAM) 2.1, MedSAM2 has been specifically adapted and fine-tuned for various 3D medical images and videos.