The Segment Anything Model (SAM) has rapidly been adopted for segmenting a wide range of natural images. However, recent studies have indicated that SAM exhibits subpar performance on 3D medical image segmentation tasks. In addition to the domain gaps between natural and medical images, disparities in the spatial arrangement between 2D and 3D images, the substantial computational burden imposed by powerful GPU servers, and the time-consuming manual prompt generation impede the extension of SAM to a broader spectrum of medical image segmentation applications. To address these challenges, in this work, we introduce a novel method, AutoSAM Adapter, designed specifically for 3D multi-organ CT-based segmentation. We employ parameter-efficient adaptation techniques in developing an automatic prompt learning paradigm to facilitate the transformation of the SAM model's capabilities to 3D medical image segmentation, eliminating the need for manually generated prompts. Furthermore, we effectively transfer the acquired knowledge of the AutoSAM Adapter to other lightweight models specifically tailored for 3D medical image analysis, achieving state-of-the-art (SOTA) performance on medical image segmentation tasks. Through extensive experimental evaluation, we demonstrate the AutoSAM Adapter as a critical foundation for effectively leveraging the emerging ability of foundation models in 2D natural image segmentation for 3D medical image segmentation.

翻译：分割一切模型（Segment Anything Model, SAM）已迅速被用于分割各类自然图像。然而，近期研究表明，SAM在3D医学图像分割任务中表现欠佳。除自然与医学图像之间的领域差异外，2D与3D图像空间排布差异、强大GPU服务器带来的沉重计算负担，以及耗时的人工提示生成过程，均阻碍了SAM扩展至更广泛的医学图像分割应用。为解决上述挑战，本文提出一种名为AutoSAM Adapter的新方法，专为基于CT的3D多器官分割设计。我们采用参数高效自适应技术，开发自动提示学习范式，以推动SAM模型能力向3D医学图像分割的迁移，无需人工生成提示。此外，我们有效将AutoSAM Adapter习得的知识转移至专为3D医学图像分析定制的轻量级模型，在医学图像分割任务中达到当前最优（State-of-the-Art, SOTA）性能。通过广泛实验评估，我们证明AutoSAM Adapter是有效利用基础模型在2D自然图像分割中涌现能力，以应对3D医学图像分割的关键基础。