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.

翻译：分割一切模型（Segment Anything Model, SAM）近期在自然图像分割领域取得了惊人成果。然而，由于自然图像与医学图像之间存在巨大的领域差异，该模型难以有效应用于医学图像分割。本文主要聚焦于超声图像分割。我们认识到，由于缺乏大规模标注的超声图像数据，训练一个用于超声图像数据的基础模型极为困难。为解决上述问题，本文开发了一种新型乳腺超声SAM适配器，称为乳腺超声分割一切模型（Breast Ultrasound Segment Anything Model, BUSSAM），该模型通过适配器技术将SAM迁移至乳腺超声图像分割领域。具体而言，我们首先设计了一种全新的卷积神经网络（CNN）图像编码器，该编码器在BUS数据集上进行了完整训练。所提出的CNN图像编码器更加轻量化，并更关注局部感受野特征，从而为SAM中的ViT分支提供互补信息。随后，我们设计了一种新型跨分支适配器，使CNN图像编码器能够与SAM模块中的ViT图像编码器充分交互。最后，我们在ViT分支中分别添加位置适配器和特征适配器，以对原始SAM进行微调。在AMUBUS与BUSI数据集上的实验结果表明，我们提出的模型显著优于其他医学图像分割模型。我们的代码将开源至：https://github.com/bscs12/BUSSAM。