  Segment Anything Model (SAM) has recently achieved amazing results in the
field of natural image segmentation. However, it is not effective for medical
image segmentation, owing to the large domain gap between natural and medical
images. In this paper, we mainly focus on ultrasound image segmentation. As we
know that it is very difficult to train a foundation model for ultrasound image
data due to the lack of large-scale annotated ultrasound image data. To address
these issues, in this paper, we develop a novel Breast Ultrasound SAM Adapter,
termed Breast Ultrasound Segment Anything Model (BUSSAM), which migrates the
SAM to the field of breast ultrasound image segmentation by using the adapter
technique. To be specific, we first design a novel CNN image encoder, which is
fully trained on the BUS dataset. Our CNN image encoder is more lightweight,
and focuses more on features of local receptive field, which provides the
complementary information to the ViT branch in SAM. Then, we design a novel
Cross-Branch Adapter to allow the CNN image encoder to fully interact with the
ViT image encoder in SAM module. Finally, we add both of the Position Adapter
and the Feature Adapter to the ViT branch to fine-tune the original SAM. The
experimental results on AMUBUS and BUSI datasets demonstrate that our proposed
model outperforms other medical image segmentation models significantly. Our
code will be available at: https://github.com/bscs12/BUSSAM.
