MedSAM2: Segment Anything in 3D Medical Images and Videos
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.
67f32c04be7fbc47d7ad7138
Jun Ma
67f32c04be7fbc47d7ad7139
Zongxin Yang
67f32c04be7fbc47d7ad713a
Sumin Kim
67f32c04be7fbc47d7ad713b
Bihui Chen
67f32c04be7fbc47d7ad713c
Mohammed Baharoon
67f32c04be7fbc47d7ad713d
Adibvafa Fallahpour
claimed_verified
2025-04-08T06:53:21.041Z
64c9888b3137cc529d0761c4
https://cdn-avatars.huggingface.co/v1/production/uploads/64c9888b3137cc529d0761c4/6n-_GMZqqssctlzuXy8H5.png
Adibvafa Fallahpour
user
adibvafa
67f32c04be7fbc47d7ad713e
Reza Asakereh
67f32c04be7fbc47d7ad713f
Hongwei Lyu
67f32c04be7fbc47d7ad7140
Bo Wang
MedSAM2: Segment Anything in 3D Medical Images and Videos
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.
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%.
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