Image segmentation is a critical task in medical image analysis, providing valuable information that helps to make an accurate diagnosis. In recent years, deep learning-based automatic image segmentation methods have achieved outstanding results in medical images. In this paper, inspired by the Segment Anything Model (SAM), a foundation model that has received much attention for its impressive accuracy and powerful generalization ability in 2D still image segmentation, we propose a SAM3D that targets at 3D volumetric medical images and utilizes the pre-trained features from the SAM encoder to capture meaningful representations of input images. Different from other existing SAM-based volumetric segmentation methods that perform the segmentation by dividing the volume into a set of 2D slices, our model takes the whole 3D volume image as input and processes it simply and effectively that avoids training a significant number of parameters. Extensive experiments are conducted on multiple medical image datasets to demonstrate that our network attains competitive results compared with other state-of-the-art methods in 3D medical segmentation tasks while being significantly efficient in terms of parameters.

借鉴了Segment Anything Model (SAM)的方法，我们提出了SAM3D，该方法针对3D体积医学图像，利用SAM编码器的预训练特征来捕捉输入图像的意义表示，并通过整体处理3D图像而避免了大量参数的训练，实验结果表明，在3D医学分割任务中，我们的网络相对于其他最先进的方法具有竞争力且参数效率明显。

SAM3D：三维医学图像中的分割模型