医学 SAM 2：通过“Segment Anything”模型对医学图像进行视频分割。

Medical SAM 2: Segment medical images as video via Segment Anything Model 2

August 1, 2024

作者: Jiayuan Zhu, Yunli Qi, Junde Wu

cs.AI

摘要

本文介绍了Medical SAM 2（MedSAM-2），这是一种先进的分割模型，利用SAM 2框架来处理2D和3D医学图像分割任务。通过采用将医学图像视为视频的理念，MedSAM-2不仅适用于3D医学图像，还开启了新的一键分割功能。这使用户可以仅提供一个或特定图像的提示，针对一个对象，之后模型可以自主地在所有后续图像中分割相同类型的对象，而不考虑图像之间的时间关系。我们评估了MedSAM-2在各种医学成像模态下的表现，包括腹部器官、视盘、脑肿瘤、甲状腺结节和皮肤病变，将其与传统和交互式分割设置中的最先进模型进行比较。我们的研究结果显示，MedSAM-2不仅在性能上超越了现有模型，而且在各种医学图像分割任务中表现出更优越的泛化能力。我们的代码将在以下网址发布：https://github.com/MedicineToken/Medical-SAM2

English

In this paper, we introduce Medical SAM 2 (MedSAM-2), an advanced segmentation model that utilizes the SAM 2 framework to address both 2D and 3D medical image segmentation tasks. By adopting the philosophy of taking medical images as videos, MedSAM-2 not only applies to 3D medical images but also unlocks new One-prompt Segmentation capability. That allows users to provide a prompt for just one or a specific image targeting an object, after which the model can autonomously segment the same type of object in all subsequent images, regardless of temporal relationships between the images. We evaluated MedSAM-2 across a variety of medical imaging modalities, including abdominal organs, optic discs, brain tumors, thyroid nodules, and skin lesions, comparing it against state-of-the-art models in both traditional and interactive segmentation settings. Our findings show that MedSAM-2 not only surpasses existing models in performance but also exhibits superior generalization across a range of medical image segmentation tasks. Our code will be released at: https://github.com/MedicineToken/Medical-SAM2