MedSAM2：三維醫學影像與視頻中的任意分割

MedSAM2: Segment Anything in 3D Medical Images and Videos

April 4, 2025

作者: Jun Ma, Zongxin Yang, Sumin Kim, Bihui Chen, Mohammed Baharoon, Adibvafa Fallahpour, Reza Asakereh, Hongwei Lyu, Bo Wang

cs.AI

摘要

醫學影像與視頻分割是精準醫療中的關鍵任務，近年來在開發針對特定任務或模態的2D影像模型以及通用模型方面取得了顯著進展。然而，針對3D影像和視頻構建通用模型並進行全面用戶研究的工作仍相對有限。本文介紹了MedSAM2，這是一個可提示的3D影像和視頻分割基礎模型。該模型通過在包含超過455,000個3D影像-掩碼對和76,000幀視頻的大型醫學數據集上微調Segment Anything Model 2而開發，在多種器官、病變和成像模態上均超越了以往模型。此外，我們實施了一個人機協作流程，以促進大規模數據集的創建，據我們所知，這項研究涉及了迄今為止最廣泛的用戶研究，包括5,000個CT病變、3,984個肝臟MRI病變和251,550幀心臟超聲視頻的註釋，結果表明MedSAM2可以將人工成本降低超過85%。MedSAM2還被集成到具有用戶友好界面的廣泛使用平台中，支持本地和雲端部署，使其成為支持研究和醫療環境中高效、可擴展和高質量分割的實用工具。

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.