To address prevalent issues in medical imaging, such as data acquisition
challenges and label availability, transfer learning from natural to medical
image domains serves as a viable strategy to produce reliable segmentation
results. However, several existing barriers between domains need to be broken
down, including addressing contrast discrepancies, managing anatomical
variability, and adapting 2D pretrained models for 3D segmentation tasks. In
this paper, we propose ProMISe,a prompt-driven 3D medical image segmentation
model using only a single point prompt to leverage knowledge from a pretrained
2D image foundation model. In particular, we use the pretrained vision
transformer from the Segment Anything Model (SAM) and integrate lightweight
adapters to extract depth-related (3D) spatial context without updating the
pretrained weights. For robust results, a hybrid network with complementary
encoders is designed, and a boundary-aware loss is proposed to achieve precise
boundaries. We evaluate our model on two public datasets for colon and pancreas
tumor segmentations, respectively. Compared to the state-of-the-art
segmentation methods with and without prompt engineering, our proposed method
achieves superior performance. The code is publicly available at
this https URL

我们提出了一种基于预训练 2D 图像模型的策略，使用单点提示来进行 3D 医学图像分割，并通过引入轻量级适配器来提取深度相关的（3D）空间上下文，以实现准确的边界。与其他分割方法相比，我们的方法在结肠和胰腺肿瘤分割方面具有卓越的性能。