MedSAM2: Segment Anything in 3D Medical Images and Videos

TL;DR

MedSAM2 is a versatile, promptable foundation model for 3D medical image and video segmentation, trained on extensive datasets, outperforming prior models and enabling efficient, large-scale annotation with significant manual cost reduction.

Contribution

We introduce MedSAM2, a general-purpose, promptable segmentation model for 3D medical images and videos, trained on a large dataset and integrated into user-friendly platforms.

Findings

Outperforms previous models across multiple organs and modalities.

Reduces manual annotation costs by over 85%.

Supports scalable, high-quality segmentation in healthcare.

Abstract

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.