结肠镜图像的准确息肉分割在结直肠癌的诊断和治疗中起着至关重要的作用。虽然基于深度学习的息肉分割模型取得了重大进展，但当应用于从不同成像设备收集的看不见的目标域数据集时，它们常常会出现性能下降的问题。为了应对这一挑战，无监督域适应（UDA）方法通过利用标记的源数据和未标记的目标数据来缩小域差距而受到关注。然而，现有的 UDA 方法主要侧重于捕获类级表示，而忽略了域级表示。此外，伪标签的不确定性可能会阻碍分割性能。为了解决这些问题，我们提出了一种新的域交互式对比学习和原型引导自我训练（DCL-PS）框架，用于跨域息肉分割。具体来说，提出了具有域混合原型更新策略的域交互式对比学习（DCL）来区分跨域的类特征表示。然后，为了增强编码器的特征提取能力，我们提出了一种基于对比学习的交叉一致性训练（CL-CCT）策略，该策略应用于由主解码器的输出和扰动的辅助输出获得的原型。此外，我们提出了一种原型引导的自训练（PS）策略，该策略在自训练过程中动态为每个像素分配权重，过滤掉不可靠的像素并提高伪标签的质量。实验结果证明了 DCL-PS 在提高目标域息肉分割性能方面的优越性。代码将发布在https://github.com/taozh2017/DCLPS。

Accurate polyp segmentation plays a critical role from colonoscopy images in the diagnosis and treatment of colorectal cancer. While deep learning-based polyp segmentation models have made significant progress, they often suffer from performance degradation when applied to unseen target domain datasets collected from different imaging devices. To address this challenge, unsupervised domain adaptation (UDA) methods have gained attention by leveraging labeled source data and unlabeled target data to reduce the domain gap. However, existing UDA methods primarily focus on capturing class-wise representations, neglecting domain-wise representations. Additionally, uncertainty in pseudo labels could hinder the segmentation performance. To tackle these issues, we propose a novel Domain-interactive Contrastive Learning and Prototype-guided Self-training (DCL-PS) framework for cross-domain polyp segmentation. Specifically, domaininteractive contrastive learning (DCL) with a domain-mixed prototype updating strategy is proposed to discriminate class-wise feature representations across domains. Then, to enhance the feature extraction ability of the encoder, we present a contrastive learning-based cross-consistency training (CL-CCT) strategy, which is imposed on both the prototypes obtained by the outputs of the main decoder and perturbed auxiliary outputs. Furthermore, we propose a prototype-guided self-training (PS) strategy, which dynamically assigns a weight for each pixel during selftraining, filtering out unreliable pixels and improving the quality of pseudo-labels. Experimental results demonstrate the superiority of DCL-PS in improving polyp segmentation performance in the target domain. The code will be released at https://github.com/taozh2017/DCLPS.