Medical image segmentation constitutes a crucial step in the analysis of medical images, possessing extensive applications and research significance within the realm of medical research and practice. Convolutional neural network achieved great success in medical image segmentation. However, acquiring large labeled datasets remains unattainable due to the substantial expertise and time required for image labeling, as well as heightened patient privacy concerns. To solve scarce medical image data, we propose a powerful network Domain Tuning SAM for Medical images (DT-SAM). We construct an encoder utilizing a parameter-effective fine-tuning strategy and SAM. This strategy selectively updates a small fraction of the weight increments while preserving the majority of the pre-training weights in the SAM encoder, consequently reducing the required number of training samples. Meanwhile, our approach leverages only SAM encoder structure while incorporating a decoder similar to U-Net decoder structure and redesigning skip connections to concatenate encoder-extracted features, which effectively decode the features extracted by the encoder and preserve edge information. We have conducted comprehensive experiments on three publicly available medical image segmentation datasets. The combined experimental results show that our method can effectively perform few shot medical image segmentation. With just one labeled data, achieving a Dice score of 63.51%, a HD of 17.94 and an IoU score of 73.55% on Heart Task, on Prostate Task, an average Dice score of 46.01%, a HD of 10.25 and an IoU score of 65.92% were achieved, and the Dice, HD, and IoU score reaching 88.67%, 10.63, and 90.19% on BUSI. Remarkably, with few training samples, our method consistently outperforms various based on SAM and CNN.

医学图像分割是医学图像分析的关键步骤，在医学研究和实践领域具有广泛的应用和研究意义。卷积神经网络在医学图像分割方面取得了巨大成功。然而，由于图像标记需要大量的专业知识和时间，以及对患者隐私的担忧加剧，因此仍然无法获得大型标记数据集。为了解决稀缺的医学图像数据问题，我们提出了一种强大的网络 Domain Tuning SAM for Medical images （DT-SAM）。我们利用参数有效的微调策略和 SAM 构建了一个编码器。此策略有选择地更新一小部分权重增量，同时保留 SAM 编码器中的大部分预训练权重，从而减少所需的训练样本数量。同时，我们的方法只利用 SAM 编码器结构，同时加入类似于 U-Net 解码器结构的解码器，并重新设计跳过连接以连接编码器提取的特征，从而有效地解码编码器提取的特征并保留边缘信息。我们对三个公开可用的医学图像分割数据集进行了全面的实验。综合实验结果表明，我们的方法可以有效地进行少镜头医学图像分割。仅用一个标记数据，心脏任务的骰子分数为 63.51%，HD 为 17.94，IoU 分数为 73.55%，前列腺任务的平均骰子分数为 46.01%，HD 为 10.25，IoU 分数为 65.92%，BUSI 的骰子、HD 和 IoU 分数达到 88.67%、10.63 和 90.19%。 值得注意的是，由于训练样本很少，我们的方法始终优于基于 SAM 和 CNN 的各种方法。