MedSAM2可一键分割3D医学影像，大幅降低人工标注成本。
MedSAM2: Segment Anything in 3D Medical Images and Videos
- 基于45万+3D图像对微调，支持多模态3D图像与视频分割
- 在5000个CT病灶、近25万帧超声心动图上实现超85%人工成本削减
- 已接入主流平台，适合科研与临床高效部署使用
医学图像与视频分割对精准医疗至关重要，尽管2D图像领域已有较多专用及通用模型进展，但针对3D图像与视频的通用模型研究仍有限，且缺乏全面用户评估。本文提出MedSAM2，一个可提示的3D医学图像与视频分割基础模型。通过在包含超过45.5万对3D图像-掩码及7.6万帧视频的数据集上微调Segment Anything Model 2，该模型在多种器官、病灶及成像模态下均优于现有方法。此外，我们构建了人机协同数据生成流程，完成了迄今为止规模最大的用户研究：标注了5,000个CT病灶、3,984个肝脏MRI病灶以及251,550帧超声心动图视频，验证了MedSAM2可使人工标注成本降低超过85%。该模型已集成至广泛使用的平台，支持本地与云端部署，具备友好的用户界面，是科研与医疗环境中实现高效、可扩展、高质量分割的实用工具。
原文摘要 · Abstract (English)
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.
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