用3D U-Net生成提示，让SAM更好分割脑部MRI中的低对比结构。
Not Quite Anything: Overcoming SAMs Limitations for 3D Medical Imaging
- 将SAM输出作为额外通道输入，结合3D U-Net生成的粗略提示
- 基底节分割体积准确率达96%，可追踪纵向变化
- 无需微调模型，适合医学影像中数据分布偏移场景
基础分割模型如SAM和SAM-2在自然图像上表现良好，但在脑部MRI中因尾状核和丘脑等结构边界模糊、对比度低而表现不佳。我们提出一种组合式方法：将基础模型输出作为额外输入通道，与MRI一同输入，以突出感兴趣区域。通过先前在MRI分割上训练的轻量级3D U-Net生成SAM-2提示，该U-Net可能来自不同数据集，其预测虽不精确但通常位于正确区域。对基础模型输出的边缘进行平滑处理，以提升与MRI的对齐效果。我们也测试了在相同框架下使用DINO注意力图实现免提示分割。该架构避免修改基础模型权重，可在不重训情况下适应领域偏移。在基底节分割上达到约96%的体积准确率，足以支持纵向体积变化研究。方法快速、标签高效，且对分布外扫描具有鲁棒性。应用于研究突发性儿童强迫症中的炎症相关变化。
原文摘要 · Abstract (English)
Foundation segmentation models such as SAM and SAM-2 perform well on natural images but struggle with brain MRIs where structures like the caudate and thalamus lack sharp boundaries and have low contrast. Rather than fine tune these models (for example MedSAM), we propose a compositional alternative where the foundation model output is treated as an additional input channel and passed alongside the MRI to highlight regions of interest. We generate SAM-2 prompts by using a lightweight 3D U-Net that was previously trained on MRI segmentation. The U-Net may have been trained on a different dataset, so its guesses are often imprecise but usually in the correct region. The edges of the resulting foundation model guesses are smoothed to improve alignment with the MRI. We also test prompt free segmentation using DINO attention maps in the same framework. This has-a architecture avoids modifying foundation weights and adapts to domain shift without retraining the foundation model. It reaches about 96 percent volume accuracy on basal ganglia segmentation, which is sufficient for our study of longitudinal volume change. The approach is fast, label efficient, and robust to out of distribution scans. We apply it to study inflammation linked changes in sudden onset pediatric OCD.
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