With the development of Deep Neural Networks (DNNs), many efforts have been made to handle medical image segmentation. Traditional methods such as nnUNet train specific segmentation models on the individual datasets. Plenty of recent methods have been proposed to adapt the foundational Segment Anything Model (SAM) to medical image segmentation. However, they still focus on discrete representations to generate pixel-wise predictions, which are spatially inflexible and scale poorly to higher resolution. In contrast, implicit methods learn continuous representations for segmentation, which is crucial for medical image segmentation. In this paper, we propose I-MedSAM, which leverages the benefits of both continuous representations and SAM, to obtain better cross-domain ability and accurate boundary delineation. Since medical image segmentation needs to predict detailed segmentation boundaries, we designed a novel adapter to enhance the SAM features with high-frequency information during Parameter-Efficient Fine-Tuning (PEFT). To convert the SAM features and coordinates into continuous segmentation output, we utilize Implicit Neural Representation (INR) to learn an implicit segmentation decoder. We also propose an uncertainty-guided sampling strategy for efficient learning of INR. Extensive evaluations on 2D medical image segmentation tasks have shown that our proposed method with only 1.6M trainable parameters outperforms existing methods including discrete and implicit methods. The code will be available at: https://github.com/ucwxb/I-MedSAM.

翻译：随着深度神经网络的发展，医学图像分割领域已涌现出诸多研究成果。传统方法（如nnUNet）通常在特定数据集上训练专用分割模型。近期大量研究致力于将基础分割模型Segment Anything（SAM）适配至医学图像分割任务。然而，这些方法仍聚焦于离散表示以生成逐像素预测，存在空间灵活性不足且难以适应高分辨率图像的局限。相比之下，隐式方法通过学习连续表示进行分割，这对医学图像分割至关重要。本文提出I-MedSAM模型，通过融合连续表示与SAM的优势，实现了更优的跨域能力与精确的边界描绘。鉴于医学图像分割需预测精细的边界轮廓，我们设计了一种新颖的适配器，在参数高效微调过程中利用高频信息增强SAM特征。为将SAM特征与坐标转换为连续分割输出，我们采用隐式神经表示学习隐式分割解码器，并提出基于不确定性的采样策略以提升INR学习效率。在二维医学图像分割任务上的大量实验表明，仅需160万可训练参数的所提方法在性能上超越了现有离散方法与隐式方法。代码已开源：https://github.com/ucwxb/I-MedSAM。