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。