Despite that the segment anything model (SAM) achieved impressive results on general-purpose semantic segmentation with strong generalization ability on daily images, its demonstrated performance on medical image segmentation is less precise and unstable, especially when dealing with tumor segmentation tasks that involve objects of small sizes, irregular shapes, and low contrast. Notably, the original SAM architecture is designed for 2D natural images and, therefore would not be able to extract the 3D spatial information from volumetric medical data effectively. In this paper, we propose a novel adaptation method for transferring SAM from 2D to 3D for promptable medical image segmentation. Through a holistically designed scheme for architecture modification, we transfer the SAM to support volumetric inputs while retaining the majority of its pre-trained parameters for reuse. The fine-tuning process is conducted in a parameter-efficient manner, wherein most of the pre-trained parameters remain frozen, and only a few lightweight spatial adapters are introduced and tuned. Regardless of the domain gap between natural and medical data and the disparity in the spatial arrangement between 2D and 3D, the transformer trained on natural images can effectively capture the spatial patterns present in volumetric medical images with only lightweight adaptations. We conduct experiments on four open-source tumor segmentation datasets, and with a single click prompt, our model can outperform domain state-of-the-art medical image segmentation models and interactive segmentation models. We also compared our adaptation method with existing popular adapters and observed significant performance improvement on most datasets. Our code and models are available at:

中文翻译：

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3DSAM 适配器：SAM 从 2D 到 3D 的整体适应，可实现快速肿瘤分割


尽管分段任何模型（SAM）在通用语义分割上取得了令人印象深刻的结果，并且对日常图像具有很强的泛化能力，但其在医学图像分割上表现出的性能不太精确且不稳定，特别是在处理涉及对象的肿瘤分割任务时。尺寸小、形状不规则、对比度低。值得注意的是，原始的 SAM 架构是为 2D 自然图像设计的，因此无法有效地从体医学数据中提取 3D 空间信息。在本文中，我们提出了一种新颖的自适应方法，用于将 SAM 从 2D 转移到 3D，以实现快速的医学图像分割。通过整体设计的架构修改方案，我们将 SAM 转移为支持体积输入，同时保留其大部分预先训练的参数以供重复使用。微调过程以参数有效的方式进行，其中大多数预训练参数保持冻结，并且仅引入和调整少数轻量级空间适配器。无论自然数据和医学数据之间的域差距以及 2D 和 3D 之间的空间排列差异如何，在自然图像上训练的转换器只需进行轻量级的调整就可以有效地捕获体积医学图像中存在的空间模式。我们在四个开源肿瘤分割数据集上进行了实验，只需单击一下提示，我们的模型就可以超越领域最先进的医学图像分割模型和交互式分割模型。我们还将我们的适应方法与现有的流行适配器进行了比较，并观察到大多数数据集上的性能显着提高。我们的代码和型号可在以下位置获取：