并非一切：克服SAMs在3D医学影像中的局限性
Not Quite Anything: Overcoming SAMs Limitations for 3D Medical Imaging
AI总结：
本文提出了一种无需微调基础模型的组合性替代方案，通过将基础模型输出作为额外输入通道来提高3D医学影像分割的准确性与鲁棒性。
摘要：
基于基础分割模型如SAM和SAM-2在自然图像上表现良好，但在脑部MRI中遇到困难，因为像尾状核和丘脑这样的结构缺乏清晰边界且对比度低。我们不通过微调这些模型（例如MedSAM），而是提出了一种组合性替代方案，将基础模型的输出作为额外的输入通道，并与MRI一起传递以突出感兴趣区域。我们通过使用先前在MRI分割上训练过的轻量3D U-Net生成SAM-2提示。U-Net可能在不同的数据集上训练，因此其猜测通常不精确，但通常在正确区域。生成的基础模型猜测的边缘被平滑以提高与MRI的对齐。我们还测试了在同一框架中使用DINO注意力图进行无提示分割。这种架构避免了修改基础权重，并且在不重新训练基础模型的情况下适应域偏移。它在基底核分割中达到了约96%的体积准确性，这对于我们的纵向体积变化研究是足够的。该方法速度快、标签效率高且对分布外扫描具有鲁棒性。我们将其应用于研究突然发作儿童强迫症中的炎症相关变化。
英文摘要：
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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