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.

翻译：图像分割是医学图像分析中的关键任务，能够提供有助于精准诊断的重要信息。近年来，基于深度学习的自动图像分割方法在医学图像中取得了显著成果。本文受Segment Anything Model（SAM）的启发——这一基础模型因在二维静态图像分割中展现出卓越精度和强大泛化能力而备受关注——提出了一种针对三维体积医学图像的SAM3D方法，利用SAM编码器的预训练特征来捕获输入图像的有意义表征。与现有其他基于SAM的体积分割方法（通过将体积分割为一组二维切片进行分割）不同，我们的模型将整个三维体积图像作为输入，以简洁高效的方式处理数据，避免了训练大量参数。通过在多个医学图像数据集上进行广泛实验，我们证明该网络在三维医学分割任务中与其他先进方法相比取得了竞争性结果，同时在参数量方面显著高效。