Intelligent medical image segmentation methods are rapidly evolving and being increasingly applied, yet they face the challenge of domain transfer, where algorithm performance degrades due to different data distributions between source and target domains. To address this, we introduce a method for zero-shot, single-prompt segmentation of 3D knee MRI by adapting Segment Anything Model 2 (SAM2), a general-purpose segmentation model designed to accept prompts and retain memory across frames of a video. By treating slices from 3D medical volumes as individual video frames, we leverage SAM2's advanced capabilities to generate motion- and spatially-aware predictions. We demonstrate that SAM2 can efficiently perform segmentation tasks in a zero-shot manner with no additional training or fine-tuning, accurately delineating structures in knee MRI scans using only a single prompt. Our experiments on the Osteoarthritis Initiative Zuse Institute Berlin (OAI-ZIB) dataset reveal that SAM2 achieves high accuracy on 3D knee bone segmentation, with a testing Dice similarity coefficient of 0.9643 on tibia. We also present results generated using different SAM2 model sizes, different prompt schemes, as well as comparative results from the SAM1 model deployed on the same dataset. This breakthrough has the potential to revolutionize medical image analysis by providing a scalable, cost-effective solution for automated segmentation, paving the way for broader clinical applications and streamlined workflows.

翻译：智能医学图像分割方法正快速发展并得到日益广泛的应用，但其面临领域迁移的挑战：由于源域与目标域数据分布不同，算法性能会出现下降。为解决这一问题，我们提出了一种零样本、单提示的3D膝关节MRI分割方法，该方法通过适配Segment Anything Model 2（SAM2）实现。SAM2是一种通用分割模型，设计用于接收提示并在视频帧间保持记忆。通过将3D医学体数据中的切片视为独立的视频帧，我们利用SAM2的高级能力生成具有运动与空间感知的预测。我们证明，SAM2能够以零样本方式高效执行分割任务，无需额外训练或微调，仅使用单个提示即可精确勾勒膝关节MRI扫描中的结构。我们在骨关节炎倡议柏林楚泽研究所（OAI-ZIB）数据集上的实验表明，SAM2在3D膝关节骨骼分割上实现了高精度，胫骨测试的Dice相似系数达到0.9643。我们还展示了使用不同SAM2模型尺寸、不同提示方案生成的结果，以及在相同数据集上部署的SAM1模型的对比结果。这一突破有望通过提供可扩展、经济高效的自动分割解决方案，彻底改变医学图像分析领域，为更广泛的临床应用和简化的工作流程铺平道路。