We introduce SAM3D, a new approach to semi-automatic zero-shot segmentation
of 3D images building on the existing Segment Anything Model. We achieve fast
and accurate segmentations in 3D images with a four-step strategy comprising:
volume slicing along non-orthogonal axes, efficient prompting in 3D, slice-wise
inference using the pretrained SAM, and recoposition and refinement in 3D. We
evaluated SAM3D performance qualitatively on an array of imaging modalities and
anatomical structures and quantify performance for specific organs in body CT
and tumors in brain MRI. By enabling users to create 3D segmentations of unseen
data quickly and with dramatically reduced manual input, these methods have the
potential to aid surgical planning and education, diagnostic imaging, and
scientific research.

SAM3D 是一种新的半自动零射击 3D 图像分割方法，通过现有的任意分割模型，利用四步策略实现在 3D 图像中快速且准确的分割，可以帮助手术规划和教育、诊断成像以及科学研究。

SAM3D：零射击半自动三维医学图像分割

SAM3D: Zero-Shot Semi-Automatic Segmentation in 3D Medical Images with  the Segment Anything Model

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。