CT和MR是目前诊断胰腺癌最常用的影像技术。 CT和MR图像中胰腺的精确分割可以为胰腺癌的诊断和治疗提供重要帮助。传统的监督分割方法需要大量带标签的CT和MR训练数据，通常费时费力。同时，由于域转移，传统的分割网络很难部署在不同的成像模态数据集上。跨域分割可以利用标记的源域数据来辅助未标记的目标域解决上述问题。本文提出了一种基于矩一致对比循环生成对抗网络（MC-CCycleGAN）的跨域胰腺分割算法。 MC-CCycleGAN是一种风格迁移网络，其中生成器的编码器用于从真实图像和风格迁移图像中提取特征，通过对比损失来约束特征提取，在风格迁移过程中充分提取输入图像的结构特征，同时消除多余的风格特征。提出了胰腺的多阶中心矩来描述其高维解剖结构，并提出了对比损失来约束矩一致性，从而保持样式转移前后胰腺结构和形状的一致性。提出了多教师知识蒸馏框架，将知识从多个教师转移到单个学生，从而提高学生网络的鲁棒性和性能。实验结果证明了我们的框架相对于最先进的域适应方法的优越性。

CT and MR are currently the most common imaging techniques for pancreatic cancer diagnosis. Accurate segmentation of the pancreas in CT and MR images can provide significant help in the diagnosis and treatment of pancreatic cancer. Traditional supervised segmentation methods require a large number of labeled CT and MR training data, which is usually time-consuming and laborious. Meanwhile, due to domain shift, traditional segmentation networks are difficult to be deployed on different imaging modality datasets. Cross-domain segmentation can utilize labeled source domain data to assist unlabeled target domains in solving the above problems. In this paper, a cross-domain pancreas segmentation algorithm is proposed based on Moment-Consistent Contrastive Cycle Generative Adversarial Networks (MC-CCycleGAN). MC-CCycleGAN is a style transfer network, in which the encoder of its generator is used to extract features from real images and style transfer images, constrain feature extraction through a contrastive loss, and fully extract structural features of input images during style transfer while eliminate redundant style features. The multi-order central moments of the pancreas are proposed to describe its anatomy in high dimensions and a contrastive loss is also proposed to constrain the moment consistency, so as to maintain consistency of the pancreatic structure and shape before and after style transfer. Multi-teacher knowledge distillation framework is proposed to transfer the knowledge from multiple teachers to a single student, so as to improve the robustness and performance of the student network. The experimental results have demonstrated the superiority of our framework over state-of-the-art domain adaptation methods.