Abstract
Polyp segmentation is vital for early colorectal cancer detection, yet traditional fully supervised methods struggle with morphological variability and domain shifts, requiring frequent retraining. Additionally, reliance on large-scale annotations is a major bottleneck due to the time-consuming and error-prone nature of polyp boundary labeling. Recently, vision foundation models like Segment Anything Model (SAM) have demonstrated strong generalizability and fine-grained boundary detection with sparse prompts, effectively addressing key polyp segmentation challenges. However, SAM's prompt-dependent nature limits automation in medical applications, since manually inputting prompts for each image is labor-intensive and time-consuming. We propose OP-SAM, a One-shot Polyp segmentation framework based on SAM that automatically generates prompts from a single annotated image, ensuring accurate and generalizable segmentation without additional annotation burdens. Our method introduces Correlation-based Prior Generation (CPG) for semantic label transfer and Scale-cascaded Prior Fusion (SPF) to adapt to polyp size variations as well as filter out noisy transfers. Instead of dumping all prompts at once, we devise Euclidean Prompt Evolution (EPE) for iterative prompt refinement, progressively enhancing segmentation quality. Extensive evaluations across five datasets validate OP-SAM's effectiveness. Notably, on Kvasir, it achieves 76.93% IoU, surpassing the state-of-the-art by 11.44%.
Abstract (translated)
结肠息肉分割对于早期发现结直肠癌至关重要，然而传统的全监督方法在处理形态变化和领域迁移时存在困难，需要频繁重新训练。此外，由于息肉边界标注耗时且易出错，对大规模注释的依赖成为了一个主要瓶颈。最近，像Segment Anything Model (SAM)这样的视觉基础模型展示了强大的泛化能力和稀疏提示下的精细边缘检测能力，有效地解决了关键的息肉分割挑战。然而，SAM基于提示的性质限制了其在医学应用中的自动化程度，因为为每张图像手动输入提示既费时又耗力。我们提出了一种名为OP-SAM的一次性息肉分割框架，该框架基于SAM，并从单个标注图像中自动生成提示，确保在无需额外注释负担的情况下进行准确且泛化的分割。 我们的方法引入了基于相关性的先验生成（CPG）用于语义标签的转移以及尺度级联先验融合（SPF），以适应息肉大小的变化并过滤掉噪声转移。我们没有一次性提供所有提示，而是设计了一种欧几里得提示演化（EPE），用于迭代式地改进提示，逐步提高分割质量。 在五个数据集上的广泛评估验证了OP-SAM的有效性。特别值得注意的是，在Kvasir数据集中，它实现了76.93%的交并比(IoU)，超过了当前最佳方法11.44个百分点。
URL
https://arxiv.org/abs/2507.16337
