The Segment Anything Model (SAM) has demonstrated strong performance in image segmentation of natural scene images. However, its effectiveness diminishes markedly when applied to specific scientific domains, such as Scanning Probe Microscope (SPM) images. This decline in accuracy can be attributed to the distinct data distribution and limited availability of the data inherent in the scientific images. On the other hand, the acquisition of adequate SPM datasets is both time-intensive and laborious as well as skill-dependent. To address these challenges, we propose an Adaptive Prompt Learning with SAM (APL-SAM) framework tailored for few-shot SPM image segmentation. Our approach incorporates two key innovations to enhance SAM: 1) An Adaptive Prompt Learning module leverages few-shot embeddings derived from limited support set to learn adaptively central representatives, serving as visual prompts. This innovation eliminates the need for time-consuming online user interactions for providing prompts, such as exhaustively marking points and bounding boxes slice by slice; 2) A multi-source, multi-level mask decoder specifically designed for few-shot SPM image segmentation is introduced, which can effectively capture the correspondence between the support and query images. To facilitate comprehensive training and evaluation, we introduce a new dataset, SPM-Seg, curated for SPM image segmentation. Extensive experiments on this dataset reveal that the proposed APL-SAM framework significantly outperforms the original SAM, achieving over a 30% improvement in terms of Dice Similarity Coefficient with only one-shot guidance. Moreover, APL-SAM surpasses state-of-the-art few-shot segmentation methods and even fully supervised approaches in performance. Code and dataset used in this study will be made available upon acceptance.

翻译：Segment Anything Model (SAM)在自然场景图像的图像分割任务中展现了卓越的性能。然而，当应用于特定科学领域，例如扫描探针显微镜（SPM）图像时，其有效性显著下降。这种准确性的下降可归因于科学图像固有的独特数据分布和有限的数据可用性。另一方面，获取充足的SPM数据集既耗时费力，又高度依赖于专业技能。为了应对这些挑战，我们提出了一种专为少样本SPM图像分割定制的基于SAM的自适应提示学习（APL-SAM）框架。我们的方法引入了两项关键创新以增强SAM：1）一个自适应提示学习模块，利用从有限支持集导出的少样本嵌入，自适应地学习中心代表作为视觉提示。这项创新消除了为提供提示（例如逐切片详尽标记点和边界框）而进行耗时的在线用户交互的需求；2）引入了一个专为少样本SPM图像分割设计的、多源多层次的掩码解码器，它能够有效捕捉支持图像与查询图像之间的对应关系。为了促进全面的训练和评估，我们引入了一个专为SPM图像分割策划的新数据集SPM-Seg。在该数据集上进行的大量实验表明，所提出的APL-SAM框架显著优于原始SAM，在仅使用单样本指导的情况下，Dice相似系数实现了超过30%的提升。此外，APL-SAM在性能上超越了最先进的少样本分割方法，甚至优于全监督方法。本研究中使用的代码和数据集将在论文被接受后公开。