We propose SAMed, a general solution for medical image segmentation. Different from the previous methods, SAMed is built upon the large-scale image segmentation model, Segment Anything Model (SAM), to explore the new research paradigm of customizing large-scale models for medical image segmentation. SAMed applies the low-rank-based (LoRA) finetuning strategy to the SAM image encoder and finetunes it together with the prompt encoder and the mask decoder on labeled medical image segmentation datasets. We also observe the warmup finetuning strategy and the AdamW optimizer lead SAMed to successful convergence and lower loss. Different from SAM, SAMed could perform semantic segmentation on medical images. Our trained SAMed model achieves 81.88 DSC and 20.64 HD on the Synapse multi-organ segmentation dataset, which is on par with the state-of-the-art methods. We conduct extensive experiments to validate the effectiveness of our design. Since SAMed only updates a small fraction of the SAM parameters, its deployment cost and storage cost are quite marginal in practical usage. The code of SAMed is available at https://github.com/hitachinsk/SAMed.

翻译：我们提出SAMed，一种面向医学图像分割的通用解决方案。与以往方法不同，SAMed基于大规模图像分割模型Segment Anything Model (SAM)构建，旨在探索将大规模模型定制用于医学图像分割的新研究范式。SAMed采用基于低秩分解的微调策略(LoRA)应用于SAM图像编码器，并与提示编码器、掩码解码器一起在标注的医学图像分割数据集上进行联合微调。我们还观察到预热微调策略与AdamW优化器可使SAMed成功收敛并获得更低损失。与SAM不同，SAMed能够对医学图像执行语义分割。我们在Synapse多器官分割数据集上训练的SAMed模型达到了81.88 DSC和20.64 HD，与当前最先进方法性能相当。通过大量实验验证了设计的有效性。由于SAMed仅更新SAM参数的极小部分，其在实际部署中的计算成本和存储成本均非常低。SAMed代码已开源至https://github.com/hitachinsk/SAMed。